{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/optic-disc-and-cup-segmentation-methods-for","title":"Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network","arxiv_id":"1704.00979","date":"2017-04-04","proceeding":null,"authors":["Artem Sevastopolsky"],"abstract":"Glaucoma is the second leading cause of blindness all over the world, with\napproximately 60 million cases reported worldwide in 2010. If undiagnosed in\ntime, glaucoma causes irreversible damage to the optic nerve leading to\nblindness. The optic nerve head examination, which involves measurement of\ncup-to-disc ratio, is considered one of the most valuable methods of structural\ndiagnosis of the disease. Estimation of cup-to-disc ratio requires segmentation\nof optic disc and optic cup on eye fundus images and can be performed by modern\ncomputer vision algorithms. This work presents universal approach for automatic\noptic disc and cup segmentation, which is based on deep learning, namely,\nmodification of U-Net convolutional neural network. Our experiments include\ncomparison with the best known methods on publicly available databases\nDRIONS-DB, RIM-ONE v.3, DRISHTI-GS. For both optic disc and cup segmentation,\nour method achieves quality comparable to current state-of-the-art methods,\noutperforming them in terms of the prediction time.","url_abs":"http://arxiv.org/abs/1704.00979v1","url_pdf":"http://arxiv.org/pdf/1704.00979v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"optic-disc-and-cup-segmentation-methods-for","repo_url":"https://github.com/nnoyuwan/optic-nerve-cnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"optic-disc-and-cup-segmentation-methods-for","repo_url":"https://github.com/seva100/optic-nerve-cnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}