Robust Segmentation of Optic Disc and Cup from Fundus Images Using Deep Neural Networks

TL;DR

This paper introduces RED-RCNN, a deep neural network that accurately segments optic disc and cup regions in fundus images, improving glaucoma diagnosis through enhanced segmentation and severity grading.

Contribution

The paper presents a novel residual encoder-decoder network based on RCNN for simultaneous OD and OC segmentation, outperforming existing methods like Mask RCNN.

Findings

RED-RCNN achieves higher sensitivity and specificity than state-of-the-art methods.

The segmentation accuracy improves glaucoma severity grading.

The approach demonstrates superior performance on public datasets.

Abstract

Optic disc (OD) and optic cup (OC) are regions of prominent clinical interest in a retinal fundus image. They are the primary indicators of a glaucomatous condition. With the advent and success of deep learning for healthcare research, several approaches have been proposed for the segmentation of important features in retinal fundus images. We propose a novel approach for the simultaneous segmentation of the OD and OC using a residual encoder-decoder network (REDNet) based regional convolutional neural network (RCNN). The RED-RCNN is motivated by the Mask RCNN (MRCNN). Performance comparisons with the state-of-the-art techniques and extensive validations on standard publicly available fundus image datasets show that RED-RCNN has superior performance compared with MRCNN. RED-RCNN results in Sensitivity, Specificity, Accuracy, Precision, Dice and Jaccard indices of 95.64%, 99.9%, 99.82%, 95.68%, 95.64%, 91.65%, respectively, for OD segmentation, and 91.44%, 99.87%, 99.83%, 85.67%, 87.48%, 78.09%, respectively, for OC segmentation. Further, we perform two-stage glaucoma severity grading using the cup-to-disc ratio (CDR) computed based on the obtained OD/OC segmentation. The superior segmentation performance of RED-RCNN over MRCNN translates to higher accuracy in glaucoma severity grading.