{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T17:02:14Z","timestamp":1790960534976,"version":"4.1.0"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:00:00Z","timestamp":1650758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Glaucoma is a leading cause of irreversible vision loss that gradually damages the optic nerve. In ophthalmic fundus images, measurements of the cup to optic disc (CD) ratio, CD area ratio, neuroretinal rim to optic disc (RD) area ratio, and rim thickness are key measures to screen for potential glaucomatous damage. We propose an automatic method using deep learning algorithms to segment the optic disc and cup and to estimate the key measures. The proposed method comprises three steps: The Region of Interest (ROI) (location of the optic disc) detection from a fundus image using Mask R-CNN, the optic disc and cup segmentation from the ROI using the proposed Multiscale Average Pooling Net (MAPNet), and the estimation of the key measures. Our segmentation results using 1099 fundus images show 0.9381 Jaccard Index (JI) and 0.9679 Dice Coefficient (DC) for the optic disc and 0.8222 JI and 0.8996 DC for the cup. The average CD, CD area, and RD ratio errors are 0.0451, 0.0376, and 0.0376, respectively. The average disc, cup, and rim radius ratio errors are 0.0500, 0.2257, and 0.2166, respectively. Our method performs well in estimating the key measures and shows potential to work within clinical pathways once fully implemented.<\/jats:p>","DOI":"10.3390\/diagnostics12051063","type":"journal-article","created":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T22:22:41Z","timestamp":1650838961000},"page":"1063","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Identifying Those at Risk of Glaucoma: A Deep Learning Approach for Optic Disc and Cup Segmentation and Their Boundary Analysis"],"prefix":"10.3390","volume":"12","author":[{"given":"Jongwoo","family":"Kim","sequence":"first","affiliation":[{"name":"Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Loc","family":"Tran","sequence":"additional","affiliation":[{"name":"Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tunde","family":"Peto","sequence":"additional","affiliation":[{"name":"Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen\u2019s University Belfast, Belfast BT7 1NN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily Y.","family":"Chew","sequence":"additional","affiliation":[{"name":"Clinical Trials Branch, Division of Epidemiology & Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,24]]},"reference":[{"key":"ref_1","unstructured":"Glaucoma Research Foundation (2021, November 15). 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