{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T18:58:33Z","timestamp":1771268313129,"version":"3.50.1"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Phys Eng Sci Med"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s13246-022-01178-4","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T11:04:47Z","timestamp":1662980687000},"page":"1111-1122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep learning approaches based improved light weight U-Net with attention module for optic disc segmentation"],"prefix":"10.1007","volume":"45","author":[{"given":"R.","family":"Shalini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5593-3949","authenticated-orcid":false,"given":"Varun P.","family":"Gopi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"issue":"18","key":"1178_CR1","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1001\/jama.2014.3192","volume":"311","author":"RN Weinreb","year":"2014","unstructured":"Weinreb RN, Aung T, Medeiros FA (2014) The pathophysiology and treatment of glaucoma: a review. JAMA 311(18):1901\u20131911","journal-title":"JAMA"},{"key":"1178_CR2","unstructured":"Kolko M (2017) Detection and prevention of blindness in patients with glaucoma is a socio-economical challenge. Ugeskrift for laeger 179(5)"},{"issue":"1","key":"1178_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/nrdp.2016.67","volume":"2","author":"RN Weinreb","year":"2016","unstructured":"Weinreb RN, Leung CK, Crowston JG, Medeiros FA, Friedman DS, Wiggs JL, Martin KR (2016) Primary open-angle glaucoma. Nat Rev Dis Primers 2(1):1\u201319","journal-title":"Nat Rev Dis Primers"},{"issue":"1","key":"1178_CR4","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.yaoo.2017.03.011","volume":"2","author":"ES Yung","year":"2017","unstructured":"Yung ES, Chang EL, Moster MR (2017) The use of optic disc and retinal nerve fiber imaging in detecting structural damage before the onset of functional loss. Adv Ophthalmol Optom 2(1):243\u2013260","journal-title":"Adv Ophthalmol Optom"},{"issue":"7\u20138","key":"1178_CR5","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1016\/j.compmedimag.2013.09.005","volume":"37","author":"MS Haleem","year":"2013","unstructured":"Haleem MS, Han L, Van Hemert J, Li B (2013) Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review. Comput Med Imaging Graph 37(7\u20138):581\u2013596","journal-title":"Comput Med Imaging Graph"},{"issue":"6","key":"1178_CR6","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1046\/j.1464-5491.2003.00954.x","volume":"20","author":"PH Scanlon","year":"2003","unstructured":"Scanlon PH, Malhotra R, Thomas G, Foy C, Kirkpatrick J, Lewis-Barned N, Harney B, Aldington S (2003) The effectiveness of screening for diabetic retinopathy by digital imaging photography and technician ophthalmoscopy. Diabetes Med 20(6):467\u2013474","journal-title":"Diabetes Med"},{"issue":"7","key":"1178_CR7","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, Wong DWK, Liu J, Cao X (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"},{"issue":"8","key":"1178_CR8","first-page":"6187","volume":"34","author":"H Veena","year":"2021","unstructured":"Veena H, Muruganandham A, Kumaran TS (2021) A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images. J King Saud Univ 34(8):6187\u20136198","journal-title":"J King Saud Univ"},{"key":"1178_CR9","doi-asserted-by":"crossref","unstructured":"Chen C, Wang W, Ozolek JA, Lages N, Altschuler SJ, Wu LF, Rohde GK (2012) A template matching approach for segmenting microscopy images. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), IEEE, pp\u00a0768\u2013771","DOI":"10.1109\/ISBI.2012.6235661"},{"key":"1178_CR10","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.media.2015.12.003","volume":"29","author":"G Kov\u00e1cs","year":"2016","unstructured":"Kov\u00e1cs G, Hajdu A (2016) A self-calibrating approach for the segmentation of retinal vessels by template matching and contour reconstruction. Med Image Anal 29:24\u201346","journal-title":"Med Image Anal"},{"issue":"16","key":"1178_CR11","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1080\/10255842.2019.1661391","volume":"22","author":"E Taghizadeh","year":"2019","unstructured":"Taghizadeh E, Terrier A, Becce F, Farron A, B\u00fcchler P (2019) Automated ct bone segmentation using statistical shape modelling and local template matching. Comput Methods Biomech Biomed Engin 22(16):1303\u20131310","journal-title":"Comput Methods Biomech Biomed Engin"},{"issue":"6","key":"1178_CR12","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.compmedimag.2009.12.008","volume":"34","author":"S Di Cataldo","year":"2010","unstructured":"Di Cataldo S, Ficarra E, Acquaviva A, Macii E (2010) Achieving the way for automated segmentation of nuclei in cancer tissue images through morphology-based approach: a quantitative evaluation. Comput Med Imaging Graph 34(6):453\u2013461","journal-title":"Comput Med Imaging Graph"},{"key":"1178_CR13","doi-asserted-by":"crossref","unstructured":"Singh D, Singh B, et al. (2014) A new morphology based approach for blood vessel segmentation in retinal images. In: 2014 Annual IEEE India Conference (INDICON), IEEE, pp 1\u20136","DOI":"10.1109\/INDICON.2014.7030686"},{"key":"1178_CR14","doi-asserted-by":"crossref","unstructured":"Issac A, Parthasarthi M, Dutta MK (2015) An adaptive threshold based algorithm for optic disc and cup segmentation in fundus images. In: 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, pp 143\u2013147","DOI":"10.1109\/SPIN.2015.7095384"},{"issue":"1","key":"1178_CR15","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s11277-019-07021-6","volume":"112","author":"R Raja","year":"2020","unstructured":"Raja R, Kumar S, Mahmood MR (2020) Color object detection based image retrieval using roi segmentation with multi-feature method. Wireless Pers Commun 112(1):169\u2013192","journal-title":"Wireless Pers Commun"},{"key":"1178_CR16","doi-asserted-by":"crossref","unstructured":"Wang P, Patel VM, Hacihaliloglu I (2018) Simultaneous segmentation and classification of bone surfaces from ultrasound using a multi-feature guided cnn. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 134\u2013142","DOI":"10.1007\/978-3-030-00937-3_16"},{"issue":"5","key":"1178_CR17","first-page":"209","volume":"9","author":"P Sharma","year":"2016","unstructured":"Sharma P, Suji J (2016) A review on image segmentation with its clustering techniques. Int J Signal Process Image Process Pattern Recognit 9(5):209\u2013218","journal-title":"Int J Signal Process Image Process Pattern Recognit"},{"issue":"1","key":"1178_CR18","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s00530-019-00626-8","volume":"26","author":"K Xia","year":"2020","unstructured":"Xia K, Gu X, Zhang Y (2020) Oriented grouping-constrained spectral clustering for medical imaging segmentation. Multimedia Syst 26(1):27\u201336","journal-title":"Multimedia Syst"},{"key":"1178_CR19","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.2003","volume":"4","author":"M Abdullah","year":"2016","unstructured":"Abdullah M, Fraz MM, Barman SA (2016) Localization and segmentation of optic disc in retinal images using circular hough transform and grow-cut algorithm. PeerJ 4:e2003","journal-title":"PeerJ"},{"key":"1178_CR20","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/2745183","author":"Y Gao","year":"2019","unstructured":"Gao Y, Yu X, Wu C, Zhou W, Lei X, Zhuang Y (2019) Automatic optic disc segmentation based on modified local image fitting model with shape prior information. J Healthcare Eng. https:\/\/doi.org\/10.1155\/2019\/2745183","journal-title":"J Healthcare Eng"},{"key":"1178_CR21","doi-asserted-by":"publisher","first-page":"148183","DOI":"10.1109\/ACCESS.2019.2946374","volume":"7","author":"Y Gao","year":"2019","unstructured":"Gao Y, Yu X, Wu C, Zhou W, Wang X, Chu H (2019) Accurate and efficient segmentation of optic disc and optic cup in retinal images integrating multi-view information. IEEE Access 7:148183\u2013148197","journal-title":"IEEE Access"},{"key":"1178_CR22","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"3","key":"1178_CR23","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1016\/j.bbe.2020.07.007","volume":"40","author":"A Khanna","year":"2020","unstructured":"Khanna A, Londhe ND, Gupta S, Semwal A (2020) A deep residual u-net convolutional neural network for automated lung segmentation in computed tomography images. Biocybern Biomed Eng 40(3):1314\u20131327","journal-title":"Biocybern Biomed Eng"},{"key":"1178_CR24","doi-asserted-by":"crossref","unstructured":"Chen X, Xu Y, Yan S, Wong DWK, Wong TY, Liu J (2015) Automatic feature learning for glaucoma detection based on deep learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 669\u2013677. Springer","DOI":"10.1007\/978-3-319-24574-4_80"},{"key":"1178_CR25","unstructured":"Bi L, Guo Y, Wang Q, Feng D, Fulham M, Kim J (2019) Automated segmentation of the optic disk and cup using dual-stage fully convolutional networks. arXiv preprint arXiv:1902.04713"},{"key":"1178_CR26","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.compmedimag.2019.02.005","volume":"74","author":"S Yu","year":"2019","unstructured":"Yu S, Xiao D, Frost S, Kanagasingam Y (2019) Robust optic disc and cup segmentation with deep learning for glaucoma detection. Comput Med Imaging Graph 74:61\u201371","journal-title":"Comput Med Imaging Graph"},{"issue":"3","key":"1178_CR27","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"},{"issue":"21","key":"1178_CR28","doi-asserted-by":"publisher","first-page":"15531","DOI":"10.1007\/s11042-019-7460-4","volume":"79","author":"M Juneja","year":"2020","unstructured":"Juneja M, Singh S, Agarwal N, Bali S, Gupta S, Thakur N, Jindal P (2020) Automated detection of Glaucoma using deep learning convolution network (G-net). Multimed Tools Appl 79(21):15531\u201353","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"1178_CR29","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3390\/sym10040087","volume":"10","author":"B Al-Bander","year":"2018","unstructured":"Al-Bander B, Williams BM, Al-Nuaimy W, Al-Taee MA, Pratt H, Zheng Y (2018) Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis. Symmetry 10(4):87","journal-title":"Symmetry"},{"key":"1178_CR30","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"12","key":"1178_CR31","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"1178_CR32","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1178_CR33","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"issue":"6","key":"1178_CR34","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2019) Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856\u20131867","journal-title":"IEEE Trans Med Imaging"},{"key":"1178_CR35","doi-asserted-by":"crossref","unstructured":"Vuola AO, Akram SU, Kannala J (2019) Mask-rcnn and u-net ensembled for nuclei segmentation. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, pp 208\u2013212","DOI":"10.1109\/ISBI.2019.8759574"},{"key":"1178_CR36","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper J, Oktay O, Schaap M, Heinrich M, Kainz B, Glocker B, Rueckert D (2019) Attention gated networks: Learning to leverage salient regions in medical images. Med Image Anal 53:197\u2013207","journal-title":"Med Image Anal"},{"issue":"8","key":"1178_CR37","doi-asserted-by":"publisher","first-page":"963","DOI":"10.3390\/rs11080963","volume":"11","author":"X Mei","year":"2019","unstructured":"Mei X, Pan E, Ma Y, Dai X, Huang J, Fan F, Du Q, Zheng H, Ma J (2019) Spectral-spatial attention networks for hyperspectral image classification. Remote Sens 11(8):963","journal-title":"Remote Sens"},{"key":"1178_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A (2020) Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D 404:132306","journal-title":"Physica D"},{"issue":"7","key":"1178_CR39","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0218942","volume":"14","author":"YW Lin","year":"2019","unstructured":"Lin YW, Zhou Y, Faghri F, Shaw MJ, Campbell RH (2019) Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory. PLoS ONE 14(7):e0218942","journal-title":"PLoS ONE"},{"issue":"3","key":"1178_CR40","doi-asserted-by":"publisher","first-page":"46","DOI":"10.3390\/pr5030046","volume":"5","author":"Y Wang","year":"2017","unstructured":"Wang Y, Velswamy K, Huang B (2017) A long-short term memory recurrent neural network based reinforcement learning controller for office heating ventilation and air conditioning systems. Processes 5(3):46","journal-title":"Processes"},{"issue":"7","key":"1178_CR41","doi-asserted-by":"publisher","first-page":"2631","DOI":"10.1109\/TCYB.2018.2831447","volume":"49","author":"Y Bin","year":"2018","unstructured":"Bin Y, Yang Y, Shen F, Xie N, Shen HT, Li X (2018) Describing video with attention-based bidirectional lstm. IEEE Trans Cybern 49(7):2631\u20132641","journal-title":"IEEE Trans Cybern"},{"key":"1178_CR42","doi-asserted-by":"crossref","unstructured":"Shankar S, Sarawagi S (2018) Posterior attention models for sequence to sequence learning. In: International Conference on Learning Representations","DOI":"10.18653\/v1\/D18-1065"},{"key":"1178_CR43","unstructured":"Sharma S, Kiros R, Salakhutdinov R (2015) Action recognition using visual attention. arXiv preprint arXiv:1511.04119"},{"key":"1178_CR44","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.cviu.2017.10.011","volume":"166","author":"Z Li","year":"2018","unstructured":"Li Z, Gavrilyuk K, Gavves E, Jain M, Snoek CG (2018) Videolstm convolves, attends and flows for action recognition. Comput Vis Image Underst 166:41\u201350","journal-title":"Comput Vis Image Underst"},{"key":"1178_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100297","volume":"18","author":"IRI Haque","year":"2020","unstructured":"Haque IRI, Neubert J (2020) Deep learning approaches to biomedical image segmentation. Inform Med Unlocked 18:100297","journal-title":"Inform Med Unlocked"},{"key":"1178_CR46","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, et\u00a0al. (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999"},{"issue":"5","key":"1178_CR47","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s11760-020-01815-z","volume":"15","author":"X Zhao","year":"2021","unstructured":"Zhao X, Wang S, Zhao J, Wei H, Xiao M, Ta N (2021) Application of an attention u-net incorporating transfer learning for optic disc and cup segmentation. SIViP 15(5):913\u2013921","journal-title":"SIViP"},{"key":"1178_CR48","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, PMLR, pp 6105\u20136114"},{"issue":"3","key":"1178_CR49","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3390\/info8030091","volume":"8","author":"Y Yu","year":"2017","unstructured":"Yu Y, Lin H, Meng J, Wei X, Guo H, Zhao Z (2017) Deep transfer learning for modality classification of medical images. Information 8(3):91","journal-title":"Information"},{"key":"1178_CR50","unstructured":"Wu C, Wu F, Qi T, Huang Y, Xie X (2021) Fastformer: Additive attention can be all you need. arXiv preprint arXiv:2108.09084"},{"key":"1178_CR51","unstructured":"Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: 3rd International Conference on Learning Representations, ICLR 2015; Conference Date: 07-05-2015 through 09-05-2015"},{"key":"1178_CR52","doi-asserted-by":"publisher","unstructured":"Luong T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Lisbon, Portugal, pp 1412\u20131421, https:\/\/doi.org\/10.18653\/v1\/D15-1166, https:\/\/aclanthology.org\/D15-1166","DOI":"10.18653\/v1\/D15-1166"},{"key":"1178_CR53","doi-asserted-by":"publisher","first-page":"29299","DOI":"10.1109\/ACCESS.2020.2972318","volume":"8","author":"BJ Bhatkalkar","year":"2020","unstructured":"Bhatkalkar BJ, Reddy DR, Prabhu S, Bhandary SV (2020) Improving the performance of convolutional neural network for the segmentation of optic disc in fundus images using attention gates and conditional random fields. IEEE Access 8:29299\u201329310","journal-title":"IEEE Access"},{"key":"1178_CR54","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"1178_CR55","doi-asserted-by":"crossref","unstructured":"Jadon S (2020) A survey of loss functions for semantic segmentation. In: 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE, pp 1\u20137","DOI":"10.1109\/CIBCB48159.2020.9277638"},{"issue":"1","key":"1178_CR56","first-page":"1004","volume":"2","author":"J Sivaswamy","year":"2015","unstructured":"Sivaswamy J, Krishnadas S, Chakravarty A, Joshi G, Tabish AS et al (2015) A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomed Imaging Data Pap 2(1):1004","journal-title":"JSM Biomed Imaging Data Pap"},{"issue":"3","key":"1178_CR57","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, Mart\u00ednez-de-la Casa JM (2008) Identification of the optic nerve head with genetic algorithms. Artif Intell Med 43(3):243\u2013259","journal-title":"Artif Intell Med"},{"issue":"3","key":"1178_CR58","doi-asserted-by":"publisher","first-page":"231","DOI":"10.5566\/ias.1155","volume":"33","author":"E Decenci\u00e8re","year":"2014","unstructured":"Decenci\u00e8re E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A et al (2014) Feedback on a publicly distributed image database: the messidor database. Image Anal Stereol 33(3):231\u2013234","journal-title":"Image Anal Stereol"},{"key":"1178_CR59","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, Naqvi SS, Ahmed M, Madni HA, Mirza J (2020) Cded-net: joint segmentation of optic disc and optic cup for glaucoma screening. IEEE Access 8:102733\u2013102747","journal-title":"IEEE Access"},{"key":"1178_CR60","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, pp 136\u2013143. Springer","DOI":"10.1007\/978-3-319-24888-2_17"},{"issue":"10","key":"1178_CR61","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, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (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":"1178_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.102001","volume":"111","author":"MK Hasan","year":"2021","unstructured":"Hasan MK, Alam MA, Elahi MTE, Roy S, Mart\u00ed R (2021) Drnet: segmentation and localization of optic disc and fovea from diabetic retinopathy image. Artif Intell Med 111:102001","journal-title":"Artif Intell Med"},{"issue":"4","key":"1178_CR63","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1109\/TMI.2013.2238244","volume":"32","author":"S Morales","year":"2013","unstructured":"Morales S, Naranjo V, Angulo J, Alca\u00f1iz M (2013) Automatic detection of optic disc based on pca and mathematical morphology. IEEE Trans Med Imaging 32(4):786\u2013796","journal-title":"IEEE Trans Med Imaging"},{"issue":"11","key":"1178_CR64","doi-asserted-by":"publisher","first-page":"2015","DOI":"10.1007\/s11517-018-1840-1","volume":"56","author":"AS Abdullah","year":"2018","unstructured":"Abdullah AS, \u00d6zok YE, Rahebi J (2018) A novel method for retinal optic disc detection using bat meta-heuristic algorithm. Med Biol Eng Comput 56(11):2015\u20132024","journal-title":"Med Biol Eng Comput"},{"issue":"3","key":"1178_CR65","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1007\/s13246-020-00883-2","volume":"43","author":"K Nija","year":"2020","unstructured":"Nija K, Anupama C, Gopi VP, Anitha V (2020) Automated segmentation of optic disc using statistical region merging and morphological operations. Phys Eng Sci Med 43(3):857\u2013869","journal-title":"Phys Eng Sci Med"},{"key":"1178_CR66","doi-asserted-by":"crossref","unstructured":"Maninis KK, Pont-Tuset J, Arbel\u00e1ez P, Van\u00a0Gool L (2016) Deep retinal image understanding. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 140\u2013148. Springer","DOI":"10.1007\/978-3-319-46723-8_17"},{"key":"1178_CR67","doi-asserted-by":"crossref","unstructured":"Nija K, Anitha V, Gopi VP (2019) An automated method of optic disc detection from retinal fundus images. In: 2019 International Conference on Contemporary Computing and Informatics (IC3I), pp 111\u2013116. IEEE","DOI":"10.1109\/IC3I46837.2019.9055543"},{"issue":"6","key":"1178_CR68","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/JBHI.2015.2473159","volume":"20","author":"S Roychowdhury","year":"2015","unstructured":"Roychowdhury S, Koozekanani DD, Kuchinka SN, Parhi KK (2015) Optic disc boundary and vessel origin segmentation of fundus images. IEEE J Biomed Health Inform 20(6):1562\u20131574","journal-title":"IEEE J Biomed Health Inform"},{"key":"1178_CR69","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.bspc.2015.10.012","volume":"25","author":"MP Sarathi","year":"2016","unstructured":"Sarathi MP, Dutta MK, Singh A, Travieso CM (2016) Blood vessel inpainting based technique for efficient localization and segmentation of optic disc in digital fundus images. Biomed Signal Process Control 25:108\u2013117","journal-title":"Biomed Signal Process Control"},{"issue":"12","key":"1178_CR70","doi-asserted-by":"publisher","first-page":"2195","DOI":"10.1007\/s11548-017-1670-x","volume":"12","author":"VP Gopi","year":"2017","unstructured":"Gopi VP, Anjali M, Niwas SI (2017) Pca-based localization approach for segmentation of optic disc. Int J Comput Assist Radiol Surg 12(12):2195\u20132204","journal-title":"Int J Comput Assist Radiol Surg"}],"container-title":["Physical and Engineering Sciences in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-022-01178-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13246-022-01178-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-022-01178-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T10:35:58Z","timestamp":1670927758000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13246-022-01178-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,12]]},"references-count":70,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["1178"],"URL":"https:\/\/doi.org\/10.1007\/s13246-022-01178-4","relation":{},"ISSN":["2662-4729","2662-4737"],"issn-type":[{"value":"2662-4729","type":"print"},{"value":"2662-4737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,12]]},"assertion":[{"value":"18 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 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 declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"For this type of study, formal consent is not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human study"}},{"value":"This article does not contain any studies with human participants or animals performed by the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}]}}