Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network

TL;DR

This paper introduces a modified U-Net deep learning model for automatic segmentation of optic disc and cup in fundus images, aiding glaucoma diagnosis with comparable accuracy and faster prediction times.

Contribution

A universal deep learning approach based on a modified U-Net for accurate and fast optic disc and cup segmentation in glaucoma detection.

Findings

Achieves segmentation quality comparable to state-of-the-art methods.

Outperforms existing methods in prediction speed.

Validated on multiple public databases.

Abstract

Glaucoma is the second leading cause of blindness all over the world, with approximately 60 million cases reported worldwide in 2010. If undiagnosed in time, glaucoma causes irreversible damage to the optic nerve leading to blindness. The optic nerve head examination, which involves measurement of cup-to-disc ratio, is considered one of the most valuable methods of structural diagnosis of the disease. Estimation of cup-to-disc ratio requires segmentation of optic disc and optic cup on eye fundus images and can be performed by modern computer vision algorithms. This work presents universal approach for automatic optic disc and cup segmentation, which is based on deep learning, namely, modification of U-Net convolutional neural network. Our experiments include comparison with the best known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS. For both optic disc and cup segmentation, our method achieves quality comparable to current state-of-the-art methods, outperforming them in terms of the prediction time.

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