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RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models
Published 16 Jan 2024 in eess.IV, cs.CV, and cs.LG | (2401.08847v2)
Abstract: Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and prone to interobserver and intraobserver biases. As such, deep learning approaches could provide automated solutions for such applications. However, the potential of these techniques is often undermined by challenges in reproducibility and generalizability, which are key barriers to their clinical adoption. This paper introduces the RIDGE checklist, a comprehensive framework designed to assess the Reproducibility, Integrity, Dependability, Generalizability, and Efficiency of deep learning-based medical image segmentation models. The RIDGE checklist is not just a tool for evaluation but also a guideline for researchers striving to improve the quality and transparency of their work. By adhering to the principles outlined in the RIDGE checklist, researchers can ensure that their developed segmentation models are robust, scientifically valid, and applicable in a clinical setting.
- Kulberg, N.S., Reshetnikov, R.V., Novik, V.P., Elizarov, A.B., Gusev, M.A., Gombolevskiy, V.A., Vladzymyrskyy, A.V., Morozov, S.P.: Inter-observer variability between readers of CT images: all for one and one for all. Digital Diagnostics 2(2), 105–118 (2021) Covert et al. [2022] Covert, E.C., Fitzpatrick, K., Mikell, J., Kaza, R.K., Millet, J.D., Barkmeier, D., Gemmete, J., Christensen, J., Schipper, M.J., Dewaraja, Y.K.: Intra-and inter-operator variability in MRI-based manual segmentation of hcc lesions and its impact on dosimetry. EJNMMI Physics 9(1), 90 (2022) Schmidt et al. [2023] Schmidt, A., Morales-Álvarez, P., Molina, R.: Probabilistic modeling of inter-and intra-observer variability in medical image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 21097–21106 (2023) Kelly et al. [2019] Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., King, D.: Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine 17, 1–9 (2019) Maleki et al. [2022] Maleki, F., Ovens, K., Gupta, R., Reinhold, C., Spatz, A., Forghani, R.: Generalizability of machine learning models: Quantitative evaluation of three methodological pitfalls. Radiology: Artificial Intelligence 5(1), 220028 (2022) Yu et al. [2022] Yu, A.C., Mohajer, B., Eng, J.: External validation of deep learning algorithms for radiologic diagnosis: a systematic review. Radiology: Artificial Intelligence 4(3), 210064 (2022) Collins et al. [2015] Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G.: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod) the tripod statement. Circulation 131(2), 211–219 (2015) Schulz et al. [2010] Schulz, K.F., Altman, D.G., Moher, D.: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Pharmacology and Pharmacotherapeutics 1(2), 100–107 (2010) Bossuyt et al. [2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. 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[2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Covert, E.C., Fitzpatrick, K., Mikell, J., Kaza, R.K., Millet, J.D., Barkmeier, D., Gemmete, J., Christensen, J., Schipper, M.J., Dewaraja, Y.K.: Intra-and inter-operator variability in MRI-based manual segmentation of hcc lesions and its impact on dosimetry. EJNMMI Physics 9(1), 90 (2022) Schmidt et al. [2023] Schmidt, A., Morales-Álvarez, P., Molina, R.: Probabilistic modeling of inter-and intra-observer variability in medical image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 21097–21106 (2023) Kelly et al. [2019] Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., King, D.: Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine 17, 1–9 (2019) Maleki et al. [2022] Maleki, F., Ovens, K., Gupta, R., Reinhold, C., Spatz, A., Forghani, R.: Generalizability of machine learning models: Quantitative evaluation of three methodological pitfalls. Radiology: Artificial Intelligence 5(1), 220028 (2022) Yu et al. [2022] Yu, A.C., Mohajer, B., Eng, J.: External validation of deep learning algorithms for radiologic diagnosis: a systematic review. Radiology: Artificial Intelligence 4(3), 210064 (2022) Collins et al. [2015] Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G.: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod) the tripod statement. Circulation 131(2), 211–219 (2015) Schulz et al. [2010] Schulz, K.F., Altman, D.G., Moher, D.: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Pharmacology and Pharmacotherapeutics 1(2), 100–107 (2010) Bossuyt et al. [2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Schmidt, A., Morales-Álvarez, P., Molina, R.: Probabilistic modeling of inter-and intra-observer variability in medical image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 21097–21106 (2023) Kelly et al. [2019] Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., King, D.: Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine 17, 1–9 (2019) Maleki et al. [2022] Maleki, F., Ovens, K., Gupta, R., Reinhold, C., Spatz, A., Forghani, R.: Generalizability of machine learning models: Quantitative evaluation of three methodological pitfalls. Radiology: Artificial Intelligence 5(1), 220028 (2022) Yu et al. [2022] Yu, A.C., Mohajer, B., Eng, J.: External validation of deep learning algorithms for radiologic diagnosis: a systematic review. Radiology: Artificial Intelligence 4(3), 210064 (2022) Collins et al. [2015] Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G.: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod) the tripod statement. Circulation 131(2), 211–219 (2015) Schulz et al. [2010] Schulz, K.F., Altman, D.G., Moher, D.: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Pharmacology and Pharmacotherapeutics 1(2), 100–107 (2010) Bossuyt et al. [2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., King, D.: Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine 17, 1–9 (2019) Maleki et al. [2022] Maleki, F., Ovens, K., Gupta, R., Reinhold, C., Spatz, A., Forghani, R.: Generalizability of machine learning models: Quantitative evaluation of three methodological pitfalls. Radiology: Artificial Intelligence 5(1), 220028 (2022) Yu et al. [2022] Yu, A.C., Mohajer, B., Eng, J.: External validation of deep learning algorithms for radiologic diagnosis: a systematic review. Radiology: Artificial Intelligence 4(3), 210064 (2022) Collins et al. [2015] Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G.: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod) the tripod statement. Circulation 131(2), 211–219 (2015) Schulz et al. [2010] Schulz, K.F., Altman, D.G., Moher, D.: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Pharmacology and Pharmacotherapeutics 1(2), 100–107 (2010) Bossuyt et al. [2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. 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[2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Maleki, F., Ovens, K., Gupta, R., Reinhold, C., Spatz, A., Forghani, R.: Generalizability of machine learning models: Quantitative evaluation of three methodological pitfalls. Radiology: Artificial Intelligence 5(1), 220028 (2022) Yu et al. [2022] Yu, A.C., Mohajer, B., Eng, J.: External validation of deep learning algorithms for radiologic diagnosis: a systematic review. Radiology: Artificial Intelligence 4(3), 210064 (2022) Collins et al. [2015] Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G.: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod) the tripod statement. Circulation 131(2), 211–219 (2015) Schulz et al. [2010] Schulz, K.F., Altman, D.G., Moher, D.: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Pharmacology and Pharmacotherapeutics 1(2), 100–107 (2010) Bossuyt et al. [2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Yu, A.C., Mohajer, B., Eng, J.: External validation of deep learning algorithms for radiologic diagnosis: a systematic review. Radiology: Artificial Intelligence 4(3), 210064 (2022) Collins et al. [2015] Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G.: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod) the tripod statement. Circulation 131(2), 211–219 (2015) Schulz et al. [2010] Schulz, K.F., Altman, D.G., Moher, D.: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Pharmacology and Pharmacotherapeutics 1(2), 100–107 (2010) Bossuyt et al. [2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G.: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod) the tripod statement. Circulation 131(2), 211–219 (2015) Schulz et al. [2010] Schulz, K.F., Altman, D.G., Moher, D.: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Pharmacology and Pharmacotherapeutics 1(2), 100–107 (2010) Bossuyt et al. [2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. 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In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. 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[2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. 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[2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G.: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod) the tripod statement. Circulation 131(2), 211–219 (2015) Schulz et al. [2010] Schulz, K.F., Altman, D.G., Moher, D.: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Pharmacology and Pharmacotherapeutics 1(2), 100–107 (2010) Bossuyt et al. [2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. 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[2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. 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[2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. 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[2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. 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In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Yu, A.C., Mohajer, B., Eng, J.: External validation of deep learning algorithms for radiologic diagnosis: a systematic review. 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Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Collins, G.S., Reitsma, J.B., Altman, D.G., Moons, K.G.: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod) the tripod statement. Circulation 131(2), 211–219 (2015) Schulz et al. [2010] Schulz, K.F., Altman, D.G., Moher, D.: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Pharmacology and Pharmacotherapeutics 1(2), 100–107 (2010) Bossuyt et al. [2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Schulz, K.F., Altman, D.G., Moher, D.: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Pharmacology and Pharmacotherapeutics 1(2), 100–107 (2010) Bossuyt et al. [2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. 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Scientific Reports 13(1), 2770 (2023) Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. 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[2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. 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In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. 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Scientific Reports 13(1), 2770 (2023) Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. 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[2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. 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[2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. 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In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. 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[2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. 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Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. 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Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. 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[2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. 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[2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. 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[2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Schulz, K.F., Altman, D.G., Moher, D.: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Journal of Pharmacology and Pharmacotherapeutics 1(2), 100–107 (2010) Bossuyt et al. [2004] Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Bossuyt, P.M., Reitsma, J.B., Bruns, D.E., Gatsonis, C.A., Glasziou, P.P., Irwig, L.M., Lijmer, J.G., Moher, D., Rennie, D., Vet, H.C.: Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Family practice 21(1), 4–10 (2004) Mongan et al. [2020] Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Mongan, J., Moy, L., Kahn Jr, C.E.: Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America (2020) Buslaev et al. [2020] Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. 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[2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. 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[2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. 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[2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. 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[2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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[2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. 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[2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. 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[2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020) Bloice et al. [2019] Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Bloice, M.D., Roth, P.M., Holzinger, A.: Biomedical image augmentation using augmentor. Bioinformatics 35(21), 4522–4524 (2019) Chen et al. [2022] Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. 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[2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. 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Scientific Reports 13(1), 2770 (2023) Chen, Y., Yang, X.-H., Wei, Z., Heidari, A.A., Zheng, N., Li, Z., Chen, H., Hu, H., Zhou, Q., Guan, Q.: Generative adversarial networks in medical image augmentation: A review. Computers in Biology and Medicine 144, 105382 (2022) Kumar et al. [2020] Kumar, V., Webb, J., Gregory, A., Meixner, D.D., Knudsen, J.M., Callstrom, M., Fatemi, M., Alizad, A.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. Ieee Access 8, 63482–63496 (2020) Almotairi et al. [2020] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. 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In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. 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Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) Sander et al. [2020] Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. 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[2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sander, J., Vos, B.D., Išgum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports 10(1), 21769 (2020) Zhang et al. [2021] Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, Y., Chan, S., Chen, J.-H., Chang, K.-T., Lin, C.-Y., Pan, H.-B., Lin, W.-C., Kwong, T., Parajuli, R., Mehta, R.S., et al.: Development of u-net breast density segmentation method for fat-sat mr images using transfer learning based on non-fat-sat model. Journal of Digital Imaging 34, 877–887 (2021) Salama and Aly [2021] Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: Automated cnn approach. Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. 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[2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. 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Alexandria Engineering Journal 60(5), 4701–4709 (2021) Sappa et al. [2021] Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. 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[2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Sappa, L.B., Okuwobi, I.P., Li, M., Zhang, Y., Xie, S., Yuan, S., Chen, Q.: Retfluidnet: Retinal fluid segmentation for SD-OCT images using convolutional neural network. Journal of Digital Imaging 34(3), 691–704 (2021) Cho et al. [2021] Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. 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[2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. 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Scientific Reports 13(1), 2770 (2023) Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023)
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Scientific Reports 13(1), 2770 (2023) Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023)
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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. 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[2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. 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Scientific Reports 13(1), 2770 (2023) Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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Scientific Reports 13(1), 2770 (2023) Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. 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Scientific Reports 13(1), 2770 (2023)
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Scientific Reports 13(1), 2770 (2023) Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. 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In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. 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[2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023)
- Cho, Y., Kim, M.J., Park, B.J., Sim, K.C., Keu, Y.S., Han, Y.E., Sung, D.J., Han, N.Y.: Active learning for efficient segmentation of liver with convolutional neural network–corrected labeling in magnetic resonance imaging–derived proton density fat fraction. Journal of Digital Imaging 34, 1225–1236 (2021) Zhang et al. [2021] Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Yu, Y.: Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition 110, 107562 (2021) Wang et al. [2021] Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: Multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. 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In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119 (2021). Springer Jalali et al. [2021] Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., Anisi, M.H.: ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Zhao et al. [2022] Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. 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[2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. 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Scientific Reports 13(1), 2770 (2023) Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. 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[2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023)
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[2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023)
- Zhao, L., Asis-Cruz, J., Feng, X., Wu, Y., Kapse, K., Largent, A., Quistorff, J., Lopez, C., Wu, D., Qing, K., et al.: Automated 3d fetal brain segmentation using an optimized deep learning approach. American Journal of Neuroradiology 43(3), 448–454 (2022) Goel et al. [2022] Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023)
- Goel, A., Shih, G., Riyahi, S., Jeph, S., Dev, H., Hu, R., Romano, D., Teichman, K., Blumenfeld, J.D., Barash, I., et al.: Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiology: Artificial Intelligence 4(2), 210205 (2022) Krishnan et al. [2022] Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023)
- Krishnan, A.P., Song, Z., Clayton, D., Gaetano, L., Jia, X., Crespigny, A., Bengtsson, T., Carano, R.A.: Joint MRI t1 unenhancing and contrast-enhancing multiple sclerosis lesion segmentation with deep learning in OPERA trials. Radiology 302(3), 662–673 (2022) Primakov et al. [2022] Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023)
- Primakov, S.P., Ibrahim, A., Timmeren, J.E., Wu, G., Keek, S.A., Beuque, M., Granzier, R.W., Lavrova, E., Scrivener, M., Sanduleanu, S., et al.: Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications 13(1), 3423 (2022) Lin et al. [2023] Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023)
- Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., et al.: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging 14(1), 14 (2023) Yeung et al. [2023] Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023)
- Yeung, M., Rundo, L., Nan, Y., Sala, E., Schönlieb, C.-B., Yang, G.: Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. Journal of Digital Imaging 36(2), 739–752 (2023) Wang et al. [2023] Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023) Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023)
- Wang, Y., Zhang, H., Wang, T., Yao, L., Zhang, G., Liu, X., Yang, G., Yuan, L.: Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine mr imaging. Scientific Reports 13(1), 2770 (2023)
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