Papers
Topics
Authors
Recent
Search
2000 character limit reached

Restoring Connectivity in Vascular Segmentation using a Learned Post-Processing Model

Published 16 Apr 2024 in eess.IV | (2404.10506v2)

Abstract: Accurate segmentation of vascular networks is essential for computer-aided tools designed to address cardiovascular diseases. Despite more than thirty years of research, it remains a challenge to obtain vascular segmentation results that preserve the connectivity of the underlying vascular network. Yet connectivity is one of the key feature of these tools. In this work, we propose a post-processing algorithm aiming to reconnect vascular structures that have been disconnected by a segmentation algorithm. Connectivity being a complex property to model explicity, we propose to learn this geometric feature either through synthetic data or annotations of the application of interest. The resulting post-processing model can be used on the output of any supervised or unsupervised vascular segmentation algorithm. We show that this post-processing effectively restores the connectivity of vascular networks both in 2D and 3D images, leading to improved overall segmentation results.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (37)
  1. E. Bullitt et al. Vessel tortuosity and brain tumor malignancy: A blinded study. Academic Radiology, 2005.
  2. A plug and play framework for curvilinear structures segmentation based on a learned reconnecting regularization. Available at SSRN 4573124.
  3. Recursive tracking of vascular tree axes in 3d medical images. International Journal of Computer Assisted Radiology and Surgery, 1:331–339, 2007.
  4. Algorithms for finding global minimizers of image segmentation and denoising models. SIAM journal on applied mathematics, 66(5):1632–1648, 2006.
  5. Accurate liver vessel segmentation via active contour model with dense vessel candidates. Computer methods and programs in biomedicine, 166:61–75, 2018.
  6. A topological loss function for deep-learning based image segmentation using persistent homology. IEEE transactions on pattern analysis and machine intelligence, 44(12):8766–8778, 2020.
  7. Retinal blood vessel segmentation by using the ms-lsdnet network and geometric skeleton reconnection method. Computers in Biology and Medicine, 153:106416, 2023.
  8. Multiscale vessel enhancement filtering. In Medical Image Computing and Computer-Assisted Intervention—MICCAI’98: First International Conference Cambridge, MA, USA, October 11–13, 1998 Proceedings 1, pages 130–137. Springer, 1998.
  9. Regularizer based on euler characteristic for retinal blood vessel segmentation. Pattern Recognition Letters, 149:83–90, 2021.
  10. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical imaging, 19(3):203–210, 2000.
  11. Topology-preserving deep image segmentation. Advances in neural information processing systems, 32, 2019.
  12. Identification and reconnection of interrupted vessels in retinal vessel segmentation. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pages 1416–1420. IEEE, 2011.
  13. OpenCCO: An Implementation of Constrained Constructive Optimization for Generating 2D and 3D Vascular Trees. Image Processing On Line, 13:258–279, 2023. https://doi.org/10.5201/ipol.2023.477.
  14. Left-ventricle quantification using residual u-net. In International Workshop on Statistical Atlases and Computational Models of the Heart, pages 371–380, 2018.
  15. Topnet: Topology preserving metric learning for vessel tree reconstruction and labelling. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI 23, pages 14–23. Springer, 2020.
  16. A benchmark framework for multi-region analysis of vesselness filters. IEEE Transactions on Medical Imaging, PP:1–1, 07 2022.
  17. Robust segmentation via topology violation detection and feature synthesis. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 67–77. Springer, 2023.
  18. Progressive minimal path method for segmentation of 2d and 3d line structures. IEEE transactions on pattern analysis and machine intelligence, 40(3):696–709, 2017.
  19. Dtu-net: Learning topological similarity for curvilinear structure segmentation. In International Conference on Information Processing in Medical Imaging, pages 654–666. Springer, 2023.
  20. n𝑛nitalic_n d variational restoration of curvilinear structures with prior-based directional regularization. IEEE Transactions on Image Processing, 28(8):3848–3859, 2019.
  21. Curvilinear structure analysis by ranking the orientation responses of path operators. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 40(2):304–317, 2018.
  22. Variational method combined with frangi vesselness for tubular object segmentation. In Computational & Mathematical Biomedical Engineering (CMBE), pages 485–488, 2015.
  23. Dense dilated network with probability regularized walk for vessel detection. IEEE Transactions on Medical Imaging, 39(5):1392–1403, 2020.
  24. Cs2-net: Deep learning segmentation of curvilinear structures in medical imaging. Medical image analysis, 67:101874, 2021.
  25. Curvilinear object segmentation in medical images based on odos filter and deep learning network. arXiv preprint arXiv:2301.07475, 2023.
  26. Corsegrec: a topology-preserving scheme for extracting fully-connected coronary arteries from ct angiography. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 670–680. Springer, 2023.
  27. Cascaded multitask u-net using topological loss for vessel segmentation and centerline extraction, 2023.
  28. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4):259–268, 1992.
  29. Cerebrovascular network segmentation of mra images with deep learning. In 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pages 768–771. IEEE, 2019.
  30. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical image analysis, 2(2):143–168, 1998.
  31. Local intensity order transformation for robust curvilinear object segmentation. IEEE Transactions on Image Processing, 31:2557–2569, 2022.
  32. cldice-a novel topology-preserving loss function for tubular structure segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16560–16569, 2021.
  33. Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging, 23(4):501–509, 2004.
  34. Deepvesselnet: Vessel segmentation, centerline prediction, and bifurcation detection in 3-d angiographic volumes. Frontiers in Neuroscience, page 1285, 2020.
  35. Job-vs: Joint brain-vessel segmentation in tof-mra images. In ISBI 2023, IEEE International Symposium on Biomedical Imaging, 18-21 April 2023, Cartagena de Indias, Colombia, Cartagena de Indias, 2023.
  36. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  37. Reconnection of interrupted curvilinear structures via cortically inspired completion for ophthalmologic images. IEEE Transactions on Biomedical Engineering, 65(5):1151–1165, 2018.

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up