M-Net with Bidirectional ConvLSTM for Cup and Disc Segmentation in Fundus Images

TL;DR

This paper introduces a modified M-Net with bidirectional ConvLSTM for joint segmentation of cup and disc in fundus images, improving glaucoma diagnosis accuracy by accurately measuring the cup-to-disc ratio.

Contribution

The paper proposes a novel deep learning model combining M-Net with bidirectional ConvLSTM for improved joint segmentation of optic cup and disc in fundus images.

Findings

Achieved a dice score of 0.92 for optic disc segmentation.

Attained 98.99% accuracy in segmenting cup and disc regions.

Demonstrated effectiveness on REFUGE2 dataset.

Abstract

Glaucoma is a severe eye disease that is known to deteriorate optic never fibers, causing cup size to increase, which could result in permanent loss of vision. Glaucoma is the second leading cause of blindness after cataract, but glaucoma being more dangerous as it is not curable. Early diagnoses and treatment of glaucoma can help to slow the progression of glaucoma and its damages. For the detection of glaucoma, the Cup to Disc ratio (CDR) provides significant information. The CDR depends heavily on the accurate segmentation of cup and disc regions. In this paper, we have proposed a modified M-Net with bidirectional convolution long short-term memory (LSTM), based on joint cup and disc segmentation. The proposed network combines features of encoder and decoder, with bidirectional LSTM. Our proposed model segments cup and disc regions based on which the abnormalities in cup to disc ratio can be observed. The proposed model is tested on REFUGE2 data, where our model achieves a dice score of 0.92 for optic disc and an accuracy of 98.99% in segmenting cup and disc regions