Deep Learning models for benign and malign Ocular Tumor Growth Estimation

Abstract: Relatively abundant availability of medical imaging data has provided significant support in the development and testing of Neural Network based image processing methods. Clinicians often face issues in selecting suitable image processing algorithm for medical imaging data. A strategy for the selection of a proper model is presented here. The training data set comprises optical coherence tomography (OCT) and angiography (OCT-A) images of 50 mice eyes with more than 100 days follow-up. The data contains images from treated and untreated mouse eyes. Four deep learning variants are tested for automatic (a) differentiation of tumor region with healthy retinal layer and (b) segmentation of 3D ocular tumor volumes. Exhaustive sensitivity analysis of deep learning models is performed with respect to the number of training and testing images using 8 eight performance indices to study accuracy, reliability/reproducibility, and speed. U-net with UVgg16 is best for malign tumor data set with treatment (having considerable variation) and U-net with Inception backbone for benign tumor data (with minor variation). Loss value and root mean square error (R.M.S.E.) are found most and least sensitive performance indices, respectively. The performance (via indices) is found to be exponentially improving regarding a number of training images. The segmented OCT-Angiography data shows that neovascularization drives the tumor volume. Image analysis shows that photodynamic imaging-assisted tumor treatment protocol is transforming an aggressively growing tumor into a cyst. An empirical expression is obtained to help medical professionals to choose a particular model given the number of images and types of characteristics. We recommend that the presented exercise should be taken as standard practice before employing a particular deep learning model for biomedical image analysis.

• The paper demonstrates that the UVgg model outperforms others in segmenting malignant ocular tumors with significant imaging variations.
• It employs a rigorous sensitivity analysis across eight performance indices to assess model accuracy and computational efficiency.
• Results emphasize the vital role of robust training datasets and suggest tailored models for benign versus malignant tumor segmentation.

Analysis of Deep Learning Models for Ocular Tumor Growth Estimation

This paper by Mayank Goswami offers a comprehensive exploration of deep learning techniques for estimating the growth of benign and malignant ocular tumors using optical coherence tomography (OCT) and OCT-Angiography (OCT-A) imaging data. The study leverages imaging data from the eyes of 50 mice to evaluate the performance of various deep convolutional neural network (CNN) models in distinguishing tumor regions from healthy retinal layers and segmenting 3D tumor volumes.

Methodology and Findings

Four deep learning architectures are compared: U-net with VGG16 backbone (UVgg), U-net coupled with Inception (UIncp), and two ResNet hybrids with U-net using Dice and Binary cross-entropy as loss functions (URsD and URsEn, respectively). The research articulates a rigorous sensitivity analysis across these models, examining accuracy, reproducibility, and computational efficiency through eight performance indices, including Dice Coefficient, F-Score, Intersection over Union (IoU), RMSE, and Hausdorff Distance.

Notably, the study identifies that the U-net with a VGG16 backbone (UVgg) performs optimately in datasets with malignant tumor growth exhibiting considerable variation and where treatment was administered. Conversely, U-net with the Inception backbone (UIncp) demonstrates proficiency in benign tumor datasets with minimal variation. Among the performance indices, Loss Value was found to be the most sensitive, whereas RMSE exhibited the least sensitivity.

A significant result highlights that the model's performance exponentially enhances with an increasing number of training images, underscoring the critical role of robust datasets in model optimization. Empirical expressions derived from the study can assist clinicians in choosing the appropriate model based on the dataset characteristics and available imaging data quantities.

Implications

The findings of this research have both practical and theoretical relevance. Practically, the models demonstrated can automate tumor segmentation, which is crucial in diagnosing and planning treatment for ocular tumors. The segmentation includes identifying neovascularization, which plays a pivotal role in tumor volume changes, thereby potentially influencing treatment protocols.

Theoretically, this paper exemplifies how deep learning models can be fine-tuned and adapted for specific imaging tasks, a concept that could be extrapolated to other medical fields. Furthermore, the comprehensive sensitivity analysis challenges the usual reliance on singular performance indices, suggesting a multifaceted approach could enhance model evaluation metrics.

Future Directions

In the field of AI and biomedical imaging, this research opens avenues for further exploration of model adaptability to varying data characteristics and performance stability across diverse datasets. Given that the reproducibility crisis remains an overarching issue in AI, continued emphasis on stability and reliability metrics, as demonstrated in this study, is imperative.

Additionally, this research underscores the potential for integrated software tools that automatically suggest the optimal deep learning framework based on initial data characteristics, potentially revolutionizing user interfaces in medical AI applications. Such developments would not only streamline the selection of appropriate models but also provide clinicians with tangible benefits in diagnostic and treatment efficiency.

In conclusion, by critically assessing the nuances and performance of multiple deep learning architectures, this work exemplifies the meticulous approach required to translate AI capabilities into effective clinical applications, particularly in the intricate domain of ocular oncology.