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