{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:40:58Z","timestamp":1780764058927,"version":"3.54.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Phys Eng Sci Med"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s13246-022-01154-y","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T12:17:01Z","timestamp":1655986621000},"page":"847-858","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Graph deep network for optic disc and optic cup segmentation for glaucoma disease using retinal imaging"],"prefix":"10.1007","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3624-802X","authenticated-orcid":false,"given":"Abhilasha","family":"Joshi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. K.","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"1154_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2018.07.012","volume":"165","author":"Y Hagiwara","year":"2018","unstructured":"Hagiwara Y, Koh JEW, Tan JH et al (2018) Computer-aided diagnosis of glaucoma using fundus images: a review. Comput Methods Programs Biomed 165:1\u201312","journal-title":"Comput Methods Programs Biomed"},{"key":"1154_CR2","doi-asserted-by":"crossref","unstructured":"Dewan MAA, Arefin MS, Ullah MA, et\u00a0al (2007) Automatic extraction of features from retinal fundus image. In: 2007 international conference on information and communication technology, IEEE, pp 47\u201351","DOI":"10.1109\/ICICT.2007.375340"},{"key":"1154_CR3","doi-asserted-by":"crossref","unstructured":"Ardizzone E, Pirrone Rr, Gambino O (2009) Optic disc positioning and blood vessels extraction on eye fundus. In: IEEE EUROCON 2009, IEEE, pp 167\u2013172","DOI":"10.1109\/EURCON.2009.5167624"},{"issue":"12","key":"1154_CR4","doi-asserted-by":"publisher","first-page":"3285","DOI":"10.1109\/TIP.2010.2052280","volume":"19","author":"AE Mahfouz","year":"2010","unstructured":"Mahfouz AE, Fahmy AS (2010) Fast localization of the optic disc using projection of image features. IEEE Trans Image Process 19(12):3285\u20133289","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"1154_CR5","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1109\/TMI.2013.2247770","volume":"32","author":"J Cheng","year":"2013","unstructured":"Cheng J, Liu J, Xu Y et al (2013) Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imaging 32(6):1019\u20131032","journal-title":"IEEE Trans Med Imaging"},{"key":"1154_CR6","doi-asserted-by":"crossref","unstructured":"Fond\u00f3n I, Valverde JF, Sarmiento A, et\u00a0al (2015) Automatic optic cup segmentation algorithm for retinal fundus images based on random forest classifier. In: IEEE EUROCON 2015-international conference on computer as a tool (EUROCON), IEEE, pp 1\u20136","DOI":"10.1109\/EUROCON.2015.7313693"},{"issue":"9","key":"1154_CR7","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1049\/iet-ipr.2015.0030","volume":"9","author":"N Muangnak","year":"2015","unstructured":"Muangnak N, Aimmanee P, Makhanov S et al (2015) Vessel transform for automatic optic disk detection in retinal images. IET Image Process 9(9):743\u2013750","journal-title":"IET Image Process"},{"key":"1154_CR8","doi-asserted-by":"crossref","unstructured":"Khaing TT, Aimmanee P (2017) Optic disk segmentation in retinal images using active contour model based on extended feature projection. In: 2017 8th international conference of information and communication technology for embedded systems (IC-ICTES), IEEE, pp 1\u20136","DOI":"10.1109\/ICTEmSys.2017.7958764"},{"key":"1154_CR9","doi-asserted-by":"crossref","unstructured":"Sun G, Zhang Z, Zhang J, et\u00a0al (2021) Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening. Neural Comput Appl pp 1\u201314","DOI":"10.1007\/s00521-021-06554-x"},{"key":"1154_CR10","first-page":"93894","volume":"9","author":"M Alghamdi","year":"2021","unstructured":"Alghamdi M, Abdel-Mottaleb M (2021) A comparative study of deep learning models for diagnosing glaucoma from fundus images. IEEE Access 9:93894\u201393906","journal-title":"IEEE Access"},{"issue":"2","key":"1154_CR11","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1109\/TBME.2019.2913211","volume":"67","author":"Y Jiang","year":"2019","unstructured":"Jiang Y, Duan L, Cheng J et al (2019) Jointrcnn: a region-based convolutional neural network for optic disc and cup segmentation. IEEE Trans Biomed Eng 67(2):335\u2013343","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"1154_CR12","doi-asserted-by":"publisher","first-page":"2476","DOI":"10.1109\/TII.2020.3000204","volume":"17","author":"R Ali","year":"2020","unstructured":"Ali R, Sheng B, Li P et al (2020) Optic disk and cup segmentation through fuzzy broad learning system for glaucoma screening. IEEE Trans Ind Inform 17(4):2476\u20132487","journal-title":"IEEE Trans Ind Inform"},{"key":"1154_CR13","doi-asserted-by":"publisher","first-page":"102733","DOI":"10.1109\/ACCESS.2020.2998635","volume":"8","author":"M Tabassum","year":"2020","unstructured":"Tabassum M, Khan TM, Arsalan M et al (2020) Cded-net: joint segmentation of optic disc and optic cup for glaucoma screening. IEEE Access 8:102733\u2013102747","journal-title":"IEEE Access"},{"key":"1154_CR14","doi-asserted-by":"publisher","first-page":"2828","DOI":"10.1109\/ACCESS.2021.3139160","volume":"10","author":"MT Islam","year":"2021","unstructured":"Islam MT, Mashfu ST, Faisal A et al (2021) Deep learning-based glaucoma detection with cropped optic cup and disc and blood vessel segmentation. IEEE Access 10:2828\u20132841","journal-title":"IEEE Access"},{"key":"1154_CR15","doi-asserted-by":"crossref","unstructured":"Fu H, Xu Y, Wong DWK, et\u00a0al (2016) Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In: 2016 IEEE 13th international symposium on biomedical imaging (ISBI), IEEE, pp 698\u2013701","DOI":"10.1109\/ISBI.2016.7493362"},{"issue":"7","key":"1154_CR16","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","volume":"37","author":"H Fu","year":"2018","unstructured":"Fu H, Cheng J, Xu Y et al (2018) Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans Med Imaging 37(7):1597\u20131605","journal-title":"IEEE Trans Med Imaging"},{"key":"1154_CR17","doi-asserted-by":"crossref","unstructured":"Zhang S, Fu H, Yan Y, et\u00a0al (2019) Attention guided network for retinal image segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, Cham, pp 797\u2013805","DOI":"10.1007\/978-3-030-32239-7_88"},{"key":"1154_CR18","doi-asserted-by":"crossref","unstructured":"Wang T, Niu S, Dong J, et\u00a0al (2020) Weakly supervised retinal detachment segmentation using deep feature propagation learning in sd-oct images. In: International workshop on ophthalmic medical image analysis, Springer, Cham, pp 146\u2013154","DOI":"10.1007\/978-3-030-63419-3_15"},{"key":"1154_CR19","doi-asserted-by":"crossref","unstructured":"Bai J, Miri MS, Liu Y, et\u00a0al (2014) Graph-based optimal multi-surface segmentation with a star-shaped prior: Application to the segmentation of the optic disc and cup. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI), IEEE, pp 525\u2013528","DOI":"10.1109\/ISBI.2014.6867924"},{"issue":"6","key":"1154_CR20","doi-asserted-by":"publisher","first-page":"3043","DOI":"10.1364\/BOE.390056","volume":"11","author":"Z Tian","year":"2020","unstructured":"Tian Z, Zheng Y, Li X et al (2020) Graph convolutional network based optic disc and cup segmentation on fundus images. Biomed Opt Express 11(6):3043\u20133057","journal-title":"Biomed Opt Express"},{"issue":"14","key":"1154_CR21","doi-asserted-by":"publisher","first-page":"4758","DOI":"10.3390\/s21144758","volume":"21","author":"D Ahmedt-Aristizabal","year":"2021","unstructured":"Ahmedt-Aristizabal D, Armin MA, Denman S et al (2021) Graph-based deep learning for medical diagnosis and analysis: past, present and future. Sensors 21(14):4758","journal-title":"Sensors"},{"key":"1154_CR22","doi-asserted-by":"crossref","unstructured":"Huang Y, Chung AC (2020) Edge-variational graph convolutional networks for uncertainty-aware disease prediction. In: International conference on medical image computing and computer-assisted intervention, Springer, Cham, pp 562\u2013572","DOI":"10.1007\/978-3-030-59728-3_55"},{"issue":"9","key":"1154_CR23","doi-asserted-by":"publisher","first-page":"4164","DOI":"10.1002\/mp.14327","volume":"47","author":"Z Tian","year":"2020","unstructured":"Tian Z, Li X, Zheng Y et al (2020) Graph-convolutional-network-based interactive prostate segmentation in mr images. Med Phys 47(9):4164\u20134176","journal-title":"Med Phys"},{"key":"1154_CR24","doi-asserted-by":"crossref","unstructured":"Sivaswamy J, Krishnadas S, Joshi GD, et\u00a0al (2014) Drishti-gs: retinal image dataset for optic nerve head (onh) segmentation. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI), IEEE, pp 53\u201356","DOI":"10.1109\/ISBI.2014.6867807"},{"key":"1154_CR25","doi-asserted-by":"crossref","unstructured":"Fumero F, Alay\u00f3n S, Sanchez JL, et\u00a0al (2011) Rim-one: an open retinal image database for optic nerve evaluation. In: 2011 24th international symposium on computer-based medical systems (CBMS), IEEE, pp 1\u20136","DOI":"10.1109\/CBMS.2011.5999143"},{"issue":"3","key":"1154_CR26","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.artmed.2008.04.005","volume":"43","author":"EJ Carmona","year":"2008","unstructured":"Carmona EJ, Rinc\u00f3n M, Garc\u00eda-Feijo\u00f3 J et al (2008) Identification of the optic nerve head with genetic algorithms. Artif Intell Med 43(3):243\u2013259","journal-title":"Artif Intell Med"},{"key":"1154_CR27","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/154860","author":"A Budai","year":"2013","unstructured":"Budai A, Bock R, Maier A et al (2013) Robust vessel segmentation in fundus images. Int J Biomed Imaging. https:\/\/doi.org\/10.1155\/2013\/154860","journal-title":"Int J Biomed Imaging"},{"issue":"3","key":"1154_CR28","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1134\/S1054661817030269","volume":"27","author":"A Sevastopolsky","year":"2017","unstructured":"Sevastopolsky A (2017) Optic disc and cup segmentation methods for glaucoma detection with modification of u-net convolutional neural network. Pattern Recognit Image Anal 27(3):618\u2013624","journal-title":"Pattern Recognit Image Anal"},{"key":"1154_CR29","doi-asserted-by":"crossref","unstructured":"Zilly JG, Buhmann JM, Mahapatra D (2015) Boosting convolutional filters with entropy sampling for optic cup and disc image segmentation from fundus images. In: International workshop on machine learning in medical imaging, Springer, Cham, pp 136\u2013143","DOI":"10.1007\/978-3-319-24888-2_17"},{"key":"1154_CR30","doi-asserted-by":"crossref","unstructured":"Maninis KK, Pont-Tuset J, Arbel\u00e1ez P, et\u00a0al (2016) Deep retinal image understanding. In: International conference on medical image computing and computer-assisted intervention, Springer, Cham, pp 140\u2013148","DOI":"10.1007\/978-3-319-46723-8_17"},{"issue":"3","key":"1154_CR31","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/s10278-018-0126-3","volume":"32","author":"J Son","year":"2019","unstructured":"Son J, Park SJ, Jung KH (2019) Towards accurate segmentation of retinal vessels and the optic disc in fundoscopic images with generative adversarial networks. J Digit Imaging 32(3):499\u2013512","journal-title":"J Digit Imaging"},{"issue":"3","key":"1154_CR32","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1109\/JBHI.2018.2852635","volume":"23","author":"A Chakravarty","year":"2018","unstructured":"Chakravarty A, Sivaswamy J (2018) Race-net: a recurrent neural network for biomedical image segmentation. IEEE J Biomed Health Inform 23(3):1151\u20131162","journal-title":"IEEE J Biomed Health Inform"},{"issue":"10","key":"1154_CR33","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu Z, Cheng J, Fu H et al (2019) Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging 38(10):2281\u20132292","journal-title":"IEEE Trans Med Imaging"},{"key":"1154_CR34","doi-asserted-by":"publisher","first-page":"64483","DOI":"10.1109\/ACCESS.2019.2917508","volume":"7","author":"Y Jiang","year":"2019","unstructured":"Jiang Y, Tan N, Peng T (2019) Optic disc and cup segmentation based on deep convolutional generative adversarial networks. IEEE Access 7:64483\u201364493","journal-title":"IEEE Access"}],"container-title":["Physical and Engineering Sciences in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-022-01154-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13246-022-01154-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-022-01154-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T09:25:51Z","timestamp":1662456351000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13246-022-01154-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,23]]},"references-count":34,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["1154"],"URL":"https:\/\/doi.org\/10.1007\/s13246-022-01154-y","relation":{},"ISSN":["2662-4729","2662-4737"],"issn-type":[{"value":"2662-4729","type":"print"},{"value":"2662-4737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,23]]},"assertion":[{"value":"25 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}