{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T08:31:26Z","timestamp":1776587486715,"version":"3.51.2"},"reference-count":15,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T00:00:00Z","timestamp":1563321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The optic cup is a physiological structure in the fundus and is a small central depression in the eye. It has a normal proportion in the optic papilla. If the ratio is large, its size may be used to determine diseases such as glaucoma or congenital myopia. The occurrence of glaucoma is generally accompanied by physical changes to the optic cup, optic disc, and optic nerve fiber layer. Therefore, accurate measurement of the optic cup is important for the detection of glaucoma. The accurate segmentation of the optic cup is essential for the measurement of the size of the optic cup relative to other structures in the eye. This paper proposes a new network architecture we call Segmentation-ResNet Seg-ResNet that takes a residual network structure as the main body, introduces a channel weighting structure that automatically adjusts the dependence of the feature channels, re-calibrates the feature channels, and introduces a set of low-level features that are combined with high-level features to improve network performance. Pre-fusion features and fused features are symmetrical. Hence, this work correlates with the concept of symmetry. Combined with the training strategy of migration learning, the segmentation accuracy is improved while speeding up network convergence. The robustness and effectiveness of the proposed method are demonstrated by testing data from the GlaucomaRepo and Drishti-GS fundus image databases.<\/jats:p>","DOI":"10.3390\/sym11070933","type":"journal-article","created":{"date-parts":[[2019,7,18]],"date-time":"2019-07-18T03:11:42Z","timestamp":1563419502000},"page":"933","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Research on the Method of Color Fundus Image Optic Cup Segmentation Based on Deep Learning"],"prefix":"10.3390","volume":"11","author":[{"given":"Zhitao","family":"Xiao","sequence":"first","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin 300387, China"},{"name":"School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5010-2596","authenticated-orcid":false,"given":"Lei","family":"Geng","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin 300387, China"},{"name":"School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin 300387, China"},{"name":"School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wu","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin 300387, China"},{"name":"School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanbei","family":"Liu","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin 300387, China"},{"name":"School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,17]]},"reference":[{"key":"ref_1","unstructured":"Li, F., and Xie, L. (2014). Chinese Ophthalmology, People\u2019s Medical Publishing House."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dutta, M.K., Mourya, A.K., Singh, A., Parthasarathi, M., Burget, R., and Riha, K. (2014, January 7\u20138). Glaucoma Detection by Segmenting the Super Pixels from Fundus Colour Retinal Images. Proceedings of the 2014 International Conference on Medical Imaging, M-Health and Emerging Communication Systems, Greater Noida, India.","DOI":"10.1109\/MedCom.2014.7005981"},{"key":"ref_3","unstructured":"Zhao, Q. (2014). Research on the Recognition Algorithm of Cup in Color Fundus Image, Beijing University of Technology."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.bspc.2015.09.003","article-title":"Segmentation of Optic Disk and Optic Cup from Digital Fundus Images for the Assessment of Glaucoma","volume":"24","author":"Mittapalli","year":"2016","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/TMI.2013.2247770","article-title":"Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening","volume":"32","author":"Cheng","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alghmdi, H., Tang, H.L., Hansen, M., O\u2019Shea, A., Al Turk, L., and Peto, T. (2015, January 29\u201330). Measurement of Optical Cup-to-Disc Ratio in Fundus Images for Glaucoma Screening. Proceedings of the 2015 IEEE International Workshop on Computational Intelligence for Multimedia Understanding, Prague, Czech Republic.","DOI":"10.1109\/IWCIM.2015.7347097"},{"key":"ref_7","unstructured":"Wong, D.W.K., Liu, J., Tan, N.M., O\u2019Shea, A., Al Turk, L., and Peto, T. (2012, January 2\u20135). Automatic Detection of the Optic Cup using Vessel Kinking in Digital Retinal Fundus Images. Proceedings of the 2012 9th IEEE International Symposium on Biomedical Imaging, Barcelona, Spain."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fondon, I., Valverde, J.F., Sarmiento, A., Abbas, Q., Jim\u00e9nez, S., and Alemany, P. (2015, January 8\u201311). Automatic Optic Cup Segmentation Algorithm for Retinal Fundus Images Based on Random Forest Classifier. Proceedings of the IEEE International Conference on Computer as a Tool, Salamanca, Spain.","DOI":"10.1109\/EUROCON.2015.7313693"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.compmedimag.2016.07.012","article-title":"Glaucoma Detection using Entropy Sampling and Ensemble Learning for Automatic Optic Cup and Disc Segmentation","volume":"55","author":"Zillya","year":"2017","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bander, B.A., Williams, B.M., Nuaimy, W.A., Al-Taee, M., Pratt, H., and Zheng, Y. (2018). Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis. Symmetry, 10.","DOI":"10.3390\/sym10040087"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1134\/S1054661817030269","article-title":"Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network","volume":"27","author":"Sevastopolsky","year":"2017","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_12","first-page":"1","article-title":"Stack-U-Net: Refinement Network for Image Segmentation on the Example of Optic Disc and Cup","volume":"7","author":"Sevastopolsky","year":"2018","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","article-title":"Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sivaswamy, J., Krishnadas, S.R., Joshi, G.D., Jain, M., and Tabish, A.U.S. (May, January 29). Drishti-GS: Retinal Image Dataset for Optic Nerve Head (ONH) Segmentation. Proceedings of the 2014 IEEE International Symposium on Biomedical Imaging, Beijing, China.","DOI":"10.1109\/ISBI.2014.6867807"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/TMI.2004.825627","article-title":"Ridge-based Vessel Segmentation in Color Images of the Retina","volume":"23","author":"Staal","year":"2004","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/7\/933\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:06:43Z","timestamp":1760188003000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/7\/933"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,17]]},"references-count":15,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["sym11070933"],"URL":"https:\/\/doi.org\/10.3390\/sym11070933","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,17]]}}}