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Glaucoma is the second leading cause of blindness all over the world, withapproximately 60 million cases reported worldwide in 2010. If undiagnosed intime, glaucoma causes irreversible damage to the optic nerve leading toblindness. The optic nerve head examination, which involves measurement ofcup-to-disc ratio, is considered one of the most valuable methods of structuraldiagnosis of the disease. Estimation of cup-to-disc ratio requires segmentationof optic disc and optic cup on eye fundus images and can be performed by moderncomputer vision algorithms. This work presents universal approach for automaticoptic disc and cup segmentation, which is based on deep learning, namely,modification of U-Net convolutional neural network. Our experiments includecomparison with the best known methods on publicly available databasesDRIONS-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.

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