{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:27:04Z","timestamp":1775665624129,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T00:00:00Z","timestamp":1683763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institutes of Health","award":["1R15GM139115-01 (RC)"],"award-info":[{"award-number":["1R15GM139115-01 (RC)"]}]},{"name":"Department of Ophthalmology, University Hospitals Cleveland Medical Center (M.Y)","award":["1R15GM139115-01 (RC)"],"award-info":[{"award-number":["1R15GM139115-01 (RC)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With recent advancements in artificial intelligence, fundus diseases can be classified automatically for early diagnosis, and this is an interest of many researchers. The study aims to detect the edges of the optic cup and the optic disc of fundus images taken from glaucoma patients, which has further applications in the analysis of the cup-to-disc ratio (CDR). We apply a modified U-Net model architecture on various fundus datasets and use segmentation metrics to evaluate the model. We apply edge detection and dilation to post-process the segmentation and better visualize the optic cup and optic disc. Our model results are based on ORIGA, RIM-ONE v3, REFUGE, and Drishti-GS datasets. Our results show that our methodology obtains promising segmentation efficiency for CDR analysis.<\/jats:p>","DOI":"10.3390\/s23104668","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T01:30:29Z","timestamp":1683855029000},"page":"4668","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Identifying the Edges of the Optic Cup and the Optic Disc in Glaucoma Patients by Segmentation"],"prefix":"10.3390","volume":"23","author":[{"given":"Srikanth","family":"Tadisetty","sequence":"first","affiliation":[{"name":"Department of Computer Science, Kent State University, Kent, OH 44242, USA"}]},{"given":"Ranjith","family":"Chodavarapu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Kent State University, Kent, OH 44242, USA"}]},{"given":"Ruoming","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Kent State University, Kent, OH 44242, USA"}]},{"given":"Robert J.","family":"Clements","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Kent State University, Kent, OH 44242, USA"}]},{"given":"Minzhong","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University Hospitals, Case Western Reserve University, Cleveland, OH 44106, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1109\/RBME.2010.2084567","article-title":"Retinal imaging and image analysis","volume":"3","author":"Abramoff","year":"2010","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1136\/bjo.2005.083527","article-title":"Worldwide glaucoma through the looking glass","volume":"90","author":"Bourne","year":"2006","journal-title":"Br. J. Ophthalmol."},{"key":"ref_3","first-page":"278","article-title":"Automated Diagnosis of Glaucoma Using Cup to Disc Ratio","volume":"7","author":"Swetha","year":"2020","journal-title":"JETIR"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ronneberger, O.F., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"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":"Bhattacharya, I., Chakrabarti, S., Reehal, H., and Lakshminarayanan, V. (2017). Advances in Optical Science and Engineering, Springer. Springer Proceedings in Physics.","DOI":"10.1007\/978-981-10-3908-9"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sun, X., Xu, Y., Tan, M., Fu, H., Zhao, W., You, T., and Liu, J. (2018, January 16\u201320). Localizing Optic Disc and Cup for Glaucoma Screening via Deep Object Detection Networks. Proceedings of the Computational Pathology and Ophthalmic Medical Image Analysis: First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Granada, Spain.","DOI":"10.1007\/978-3-030-00949-6_28"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.eswa.2019.03.009","article-title":"Optic disc and optic cup segmentation from retinal images using hybrid approach","volume":"127","author":"Thakur","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Kim, J., Tran, L.Q., Chew, E.Y., and Antani, S.K. (2019, January 5\u20137). Optic Disc and Cup Segmentation for Glaucoma Characterization Using Deep Learning. Proceedings of the 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Cordoba, Spain.","DOI":"10.1109\/CBMS.2019.00100"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compmedimag.2019.02.005","article-title":"Robust optic disc and cup segmentation with deep learning for glaucoma detection","volume":"74","author":"Yu","year":"2019","journal-title":"Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Al-Bander, B., Williams, B.M., Al-Nuaimy, W., Al-Taee, M.A., 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_13","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.1007\/s11517-020-02237-2","article-title":"Automated glaucoma screening method based on image segmentation and feature extraction","volume":"58","author":"Guo","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Bajwa, M.N.S., Singh, G.A.P., Neumeier, W., Malik, M.I., Dengel, A., and Ahmed, S. (2020, January 19\u201324). G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207664"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1601354","DOI":"10.1155\/2022\/1601354","article-title":"Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model","volume":"2022","author":"Sudhan","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15531","DOI":"10.1007\/s11042-019-7460-4","article-title":"Automated detection of Glaucoma using deep learning convolution network (G-net)","volume":"79","author":"Juneja","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12361","DOI":"10.1038\/s41598-022-16262-8","article-title":"Multi-task deep learning for glaucoma detection from color fundus images","volume":"12","author":"Pascal","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1109\/TBME.2019.2913211","article-title":"JointRCNN: A Region-Based Convolutional Neural Network for Optic Disc and Cup Segmentation","volume":"67","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"CE-Net: Context Encoder Network for 2D Medical Image Segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7868","DOI":"10.1038\/s41598-022-11852-y","article-title":"MEA-Net: Multilayer edge attention network for medical image segmentation","volume":"12","author":"Liu","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bajwa, M.N., Malik, M.I., Siddiqui, S.A., Dengel, A., Shafait, F., Neumeier, W., and Ahmed, S. (2019). Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Med. Inform. Decis. Mak., 19.","DOI":"10.1186\/s12911-019-0842-8"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1007\/s10916-020-01561-2","article-title":"Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks","volume":"44","author":"Xie","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gao, J., Jiang, Y., Zhang, H., and Wang, F. (2020). Joint disc and cup segmentation based on recurrent fully convolutional network. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0238983"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108347","DOI":"10.1016\/j.asoc.2021.108347","article-title":"End-to-end multi-task learning for simultaneous optic disc and cup segmentation and glaucoma classification in eye fundus images","volume":"116","author":"Hervella","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_26","first-page":"44","article-title":"Optical Cup and Disc Segmentation using Deep Learning Technique for Glaucoma Detection","volume":"14","author":"Parkhi","year":"2023","journal-title":"Int. J. Next Gener. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1139181","DOI":"10.3389\/fnins.2023.1139181","article-title":"EARDS: EfficientNet and attention-based residual depth-wise separable convolution for joint OD and OC segmentation","volume":"17","author":"Zhou","year":"2023","journal-title":"Front. Neurosci."},{"key":"ref_28","unstructured":"Wu, J., Fu, R., Fang, H., Zhang, Y., and Xu, Y. (2023). MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sun, G., Zhang, Z., Zhang, J., Zhu, M., Zhu, X., Yang, J., and Li, Y. (2021). Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening. Neural Comput. Appl.","DOI":"10.1007\/s00521-021-06554-x"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xue, X., Wang, L., Du, W., Fujiwara, Y., and Peng, Y. (2022). Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images. Sensors, 22.","DOI":"10.3390\/s22186899"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106067","DOI":"10.1016\/j.compbiomed.2022.106067","article-title":"Optic disc detection and segmentation using saliency mask in retinal fundus images","volume":"150","author":"Zaaboub","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"106530","DOI":"10.1016\/j.cmpb.2021.106530","article-title":"ECSD-Net: A joint optic disc and cup segmentation and glaucoma classification network based on unsupervised domain adaptation","volume":"213","author":"Liu","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102261","DOI":"10.1016\/j.artmed.2022.102261","article-title":"Weak label based Bayesian U-Net for optic disc segmentation in fundus images","volume":"126","author":"Xiong","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104906","DOI":"10.1016\/j.bspc.2023.104906","article-title":"Towards an extended EfficientNet-based U-Net framework for joint optic disc and cup segmentation in the fundus image","volume":"85","author":"Wang","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sivaswamy, J., Krishnadas, S.R., Joshi, G.D., Jain, M., and Tabish, A.U. (May, January 29). Drishti-gs: Retinal image dataset for optic nerve head(onh) segmentation. Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, China.","DOI":"10.1109\/ISBI.2014.6867807"},{"key":"ref_36","first-page":"1","article-title":"A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis","volume":"2","author":"Sivaswamy","year":"2015","journal-title":"JSM Biomed. Imaging Data Pap."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s12938-019-0649-y","article-title":"CNNs for automatic glaucoma assessment using fundus images: An extensive validation","volume":"18","author":"Morales","year":"2019","journal-title":"Biomed. Eng. Online"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1109\/TMI.2019.2903434","article-title":"Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment","volume":"38","author":"Colomer","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_39","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":"Zilly","year":"2017","journal-title":"Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Phasuk, S., Tantibundhit, C., Poopresert, P., Yaemsuk, A., Suvannachart, P., Itthipanichpong, R., Chansangpetch, S., Manassakorn, A., Tantisevi, V., and Rojanapongpun, P. (2019, January 23\u201327). Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857136"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, J., Yan, Y., Xu, Y., Zhao, W., Min, H., Tan, M., and Liu, J. (2019, January 23\u201327). Conditional Adversarial Transfer for Glaucoma Diagnosis. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857308"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, X., Xu, Y., Wong, D.W.K., Wong, T.Y., and Liu, J. (2015, January 25\u201329). Glaucoma detection based on deep convolutional neural network. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy.","DOI":"10.1109\/EMBC.2015.7318462"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, A., Cheng, J., Wong, D.W.K., and Liu, J. (2016, January 16\u201320). Integrating holistic and local deep features for glaucoma classification. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590952"},{"key":"ref_44","unstructured":"Fumero, F., Sigut, J.F., Alay\u00f3n, S., Gonzalez-Hernandez, M., and Rosa, M.G. (2015, January 8\u201312). Interactive Tool and Database for Optic Disc and Cup Segmentation of Stereo and Monocular Retinal Fundus Images. Proceedings of the 23rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2015 in Co-Operation with EUROGRAPHICS, Pilsen, Czech Republic. Available online: http:\/\/wscg.zcu.cz\/DL\/wscg_DL.htm."},{"key":"ref_45","first-page":"318","article-title":"Automatic Identification of Glaucoma Using Deep Learning Methods","volume":"Volume 245","author":"Cerentini","year":"2017","journal-title":"MEDINFO 2017: Precision Healthcare Through Informatics: Proceedings of the 16th World Congress on Medical and Health Informatics"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"101570","DOI":"10.1016\/j.media.2019.101570","article-title":"Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs","volume":"59","author":"Orlando","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/S0031-3203(00)00023-6","article-title":"On the Canny edge detector","volume":"34","author":"Ding","year":"2001","journal-title":"Pattern Recognit."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1109\/TMI.2019.2899910","article-title":"Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation","volume":"38","author":"Wang","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hervella, \u00c1.S., Ramos, L., Rouco, J., Novo, J., and Ortega, M. (2020, January 4\u20138). Multi-Modal Self-Supervised Pre-Training for Joint Optic Disc and Cup Segmentation in Eye Fundus Images. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053551"},{"key":"ref_50","unstructured":"Liu, P., Kong, B., Li, Z., Zhang, S., and Fang, R. (2019). Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2019, Springer. MICCAI 2019. Lecture Notes in Computer Science."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Almubarak, H., Bazi, Y., and Alajlan, N. (2020). Two-Stage Mask-RCNN Approach for Detecting and Segmenting the Optic Nerve Head, Optic Disc, and Optic Cup in Fundus Images. Appl. Sci., 10.","DOI":"10.3390\/app10113833"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Shah, S., Kasukurthi, N., and Pande, H. (2019, January 8\u201311). Dynamic region proposal networks for semantic segmentation in automated glaucoma screening. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759171"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1007\/s11548-020-02144-9","article-title":"WGAN domain adaptation for the joint optic disc-and-cup segmentation in fundus images","volume":"15","author":"Kadambi","year":"2020","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"64483","DOI":"10.1109\/ACCESS.2019.2917508","article-title":"Optic Disc and Cup Segmentation Based on Deep Convolutional Generative Adversarial Networks","volume":"7","author":"Jiang","year":"2019","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4668\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:33:02Z","timestamp":1760124782000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4668"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,11]]},"references-count":54,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104668"],"URL":"https:\/\/doi.org\/10.3390\/s23104668","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,11]]}}}