Laceration assessment: advanced segmentation and classification framework for retinal disease categorization in optical coherence tomography images.

Abstract

Disorders affecting the retina pose a considerable risk to human vision, with an array of factors including aging, diabetes, hypertension, obesity, ocular trauma, and tobacco use exacerbating this issue in contemporary times. Optical coherence tomography (OCT) is a rapidly developing imaging modality that is capable of identifying early signs of vascular, ocular, and central nervous system abnormalities. OCT can diagnose retinal diseases through image classification, but quantifying the laceration area requires image segmentation. To overcome this obstacle, we have developed an innovative deep learning framework that can perform both tasks simultaneously. The suggested framework employs a parallel mask-guided convolutional neural network (PM-CNN) for the classification of OCT B-scans and a grade activation map (GAM) output from the PM-CNN to help a V-Net network (GAM V-Net) to segment retinal lacerations. The guiding mask for the PM-CNN is obtained from the auxiliary segmentation job. The effectiveness of the dual framework was evaluated using a combined dataset that encompassed four publicly accessible datasets along with an additional real-time dataset. This compilation included 11 categories of retinal diseases. The four publicly available datasets provided a robust foundation for the validation of the dual framework, while the real-time dataset enabled the framework's performance to be assessed on a broader range of retinal disease categories. The segmentation Dice coefficient was 78.33±0.15%, while the classification accuracy was 99.10±0.10%. The model's ability to effectively segment retinal fluids and identify retinal lacerations on a different dataset was an excellent demonstration of its generalizability.