{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:56:24Z","timestamp":1792000584760,"version":"4.4.0"},"reference-count":112,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82272086"],"award-info":[{"award-number":["82272086"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1016\/j.compmedimag.2025.102539","type":"journal-article","created":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T17:05:28Z","timestamp":1743959128000},"page":"102539","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":28,"special_numbering":"C","title":["Retinal OCT image segmentation with deep learning: A review of advances, datasets, and evaluation metrics"],"prefix":"10.1016","volume":"123","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6195-4615","authenticated-orcid":false,"given":"Huihong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanqian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoling","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianhang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Risa","family":"Higashita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2025.102539_b1","doi-asserted-by":"crossref","DOI":"10.31661\/jbpe.v0i0.1234","article-title":"An update on choroidal layer segmentation methods in optical coherence tomography images: a review","author":"Alizadeh Eghtedar","year":"2022","journal-title":"J. Biomed. Phys. Eng."},{"key":"10.1016\/j.compmedimag.2025.102539_b2","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/978-3-030-16638-0_3","article-title":"Optical coherence tomography (OCT): principle and technical realization","author":"Aumann","year":"2019","journal-title":"High Resolut. Imaging Microsc. Ophthalmology: New Front. Biomed. Opt."},{"issue":"8","key":"10.1016\/j.compmedimag.2025.102539_b3","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TMI.2019.2901398","article-title":"RETOUCH: The retinal OCT fluid detection and segmentation benchmark and challenge","volume":"38","author":"Bogunovi\u0107","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"10.1016\/j.compmedimag.2025.102539_b4","doi-asserted-by":"crossref","first-page":"7348","DOI":"10.1364\/BOE.428140","article-title":"Multi-task learning approach for volumetric segmentation and reconstruction in 3D OCT images","volume":"12","author":"Cahyo","year":"2021","journal-title":"Biomed. Opt. Express"},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102539_b5","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1109\/JBHI.2022.3225425","article-title":"FourierNet: Shape-preserving network for henle\u2019s fiber layer segmentation in optical coherence tomography images","volume":"27","author":"Cansiz","year":"2023","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102539_b6","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1364\/BOE.510464","article-title":"Self-attention CNN for retinal layer segmentation in OCT","volume":"15","author":"Cao","year":"2024","journal-title":"Biomed. Opt. Express"},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102539_b7","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1109\/TPAMI.2022.3201576","article-title":"Semi-supervised and unsupervised deep visual learning: A survey","volume":"46","author":"Chen","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102539_b8","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compmedimag.2025.102539_b9","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.media.2019.03.009","article-title":"Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis","volume":"54","author":"Cheplygina","year":"2019","journal-title":"Med. Image Anal."},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102539_b10","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1364\/BOE.6.001172","article-title":"Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema","volume":"6","author":"Chiu","year":"2015","journal-title":"Biomed. Opt. Express"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102539_b11","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1167\/iovs.11-7640","article-title":"Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images","volume":"53","author":"Chiu","year":"2012","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"10.1016\/j.compmedimag.2025.102539_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104810","article-title":"Classification and segmentation of OCT images for age-related macular degeneration based on dual guidance networks","volume":"84","author":"Diao","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.compmedimag.2025.102539_b13","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.compmedimag.2025.102539_b14","series-title":"International Workshop on Ophthalmic Medical Image Analysis","first-page":"135","article-title":"Dataset and evaluation algorithm design for goals challenge","author":"Fang","year":"2022"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102539_b15","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.ophtha.2013.07.013","article-title":"Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography","volume":"121","author":"Farsiu","year":"2014","journal-title":"Ophthalmology"},{"key":"10.1016\/j.compmedimag.2025.102539_b16","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022","first-page":"320","article-title":"SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors","author":"Fazekas","year":"2022"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102539_b17","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/JBHI.2022.3217962","article-title":"Segmentation of Bruch\u2019s membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification","volume":"27","author":"Fazekas","year":"2023","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"issue":"7","key":"10.1016\/j.compmedimag.2025.102539_b18","doi-asserted-by":"crossref","first-page":"1818","DOI":"10.1038\/s41433-021-01437-w","article-title":"Biomechanical properties of retina and choroid: a comprehensive review of techniques and translational relevance","volume":"35","author":"Ferrara","year":"2021","journal-title":"Eye"},{"issue":"Suppl 1","key":"10.1016\/j.compmedimag.2025.102539_b19","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","article-title":"A survey of uncertainty in deep neural networks","volume":"56","author":"Gawlikowski","year":"2023","journal-title":"Artif. Intell. Rev."},{"issue":"11","key":"10.1016\/j.compmedimag.2025.102539_b20","doi-asserted-by":"crossref","first-page":"5483","DOI":"10.1109\/JBHI.2023.3313392","article-title":"Automatic segmentation of retinal layers in multiple neurodegenerative disorder scenarios","volume":"27","author":"Gende","year":"2023","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"key":"10.1016\/j.compmedimag.2025.102539_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2019.106532","article-title":"OCTID: Optical coherence tomography image database","volume":"81","author":"Gholami","year":"2020","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.compmedimag.2025.102539_b22","series-title":"Optical Coherence Tomography for Ophthalmology Market Analysis","author":"Global Growth Insights","year":"2024"},{"key":"10.1016\/j.compmedimag.2025.102539_b23","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI","first-page":"351","article-title":"Unsupervised domain adaptation with contrastive learning for OCT segmentation","author":"Gomariz","year":"2022"},{"key":"10.1016\/j.compmedimag.2025.102539_b24","series-title":"Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014","first-page":"2672","article-title":"Generative adversarial nets","author":"Goodfellow","year":"2014"},{"issue":"10","key":"10.1016\/j.compmedimag.2025.102539_b25","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":"10.1016\/j.compmedimag.2025.102539_b26","series-title":"Atlas of Fundus Imaging with Optical Coherence Tomography","first-page":"351","author":"Guanglu","year":"2009"},{"key":"10.1016\/j.compmedimag.2025.102539_b27","series-title":"Image Analysis and Recognition: 15th International Conference, ICIAR 2018, P\u00f3voa de Varzim, Portugal, June 27\u201329, 2018, Proceedings 15","first-page":"695","article-title":"BIOMISA retinal image database for macular and ocular syndromes","author":"Hassan","year":"2018"},{"issue":"5","key":"10.1016\/j.compmedimag.2025.102539_b28","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1364\/BOE.487518","article-title":"Longitudinal deep network for consistent OCT layer segmentation","volume":"14","author":"He","year":"2023","journal-title":"Biomed. Opt. Express"},{"key":"10.1016\/j.compmedimag.2025.102539_b29","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","first-page":"120","article-title":"Fully convolutional boundary regression for retina OCT segmentation","author":"He","year":"2019"},{"key":"10.1016\/j.compmedimag.2025.102539_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101856","article-title":"Structured layer surface segmentation for retina OCT using fully convolutional regression networks","volume":"68","author":"He","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2025.102539_b31","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1016\/j.dib.2018.12.073","article-title":"Retinal layer parcellation of optical coherence tomography images: Data resource for multiple sclerosis and healthy controls","volume":"22","author":"He","year":"2019","journal-title":"Data Brief"},{"key":"10.1016\/j.compmedimag.2025.102539_b32","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1109\/TIP.2022.3148814","article-title":"Intra-and inter-slice contrastive learning for point supervised oct fluid segmentation","volume":"31","author":"He","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.compmedimag.2025.102539_b33","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/TIP.2022.3228163","article-title":"Structure-guided cross-attention network for cross-domain OCT fluid segmentation","volume":"32","author":"He","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.compmedimag.2025.102539_b34","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.media.2019.05.002","article-title":"Automated segmentation of macular edema in OCT using deep neural networks","volume":"55","author":"Hu","year":"2019","journal-title":"Med. Image Anal."},{"issue":"5035","key":"10.1016\/j.compmedimag.2025.102539_b35","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1126\/science.1957169","article-title":"Optical coherence tomography","volume":"254","author":"Huang","year":"1991","journal-title":"Science"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102539_b36","doi-asserted-by":"crossref","first-page":"45","DOI":"10.4103\/2228-7477.114321","article-title":"A review of algorithms for segmentation of optical coherence tomography from retina","volume":"3","author":"Kafieh","year":"2013","journal-title":"J. Med. Signals & Sensors"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102539_b37","doi-asserted-by":"crossref","first-page":"43","DOI":"10.4103\/2228-7477.199157","article-title":"Isfahan MISP dataset","volume":"7","author":"Kashefpur","year":"2017","journal-title":"J. Med. Signals & Sensors"},{"issue":"5","key":"10.1016\/j.compmedimag.2025.102539_b38","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102539_b39","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2012","journal-title":"Commun. ACM"},{"key":"10.1016\/j.compmedimag.2025.102539_b40","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","first-page":"431","article-title":"Projective skip-connections for segmentation along a subset of dimensions in retinal OCT","author":"Lachinov","year":"2021"},{"key":"10.1016\/j.compmedimag.2025.102539_b41","series-title":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2","article-title":"Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories","author":"Lazebnik","year":"2006"},{"issue":"7553","key":"10.1016\/j.compmedimag.2025.102539_b42","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"11","key":"10.1016\/j.compmedimag.2025.102539_b43","doi-asserted-by":"crossref","first-page":"3343","DOI":"10.1109\/TMI.2020.2992244","article-title":"Image projection network: 3D to 2D image segmentation in OCTA images","volume":"39","author":"Li","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102539_b44","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1364\/BOE.417212","article-title":"Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary OCT images","volume":"12","author":"Li","year":"2021","journal-title":"Biomed. Opt. Express"},{"key":"10.1016\/j.compmedimag.2025.102539_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102805","article-title":"Self-supervised anomaly detection, staging and segmentation for retinal images","volume":"87","author":"Li","year":"2023","journal-title":"Med. Image Anal."},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102539_b46","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1167\/tvst.9.2.61","article-title":"DeepRetina: Layer Segmentation of Retina in OCT Images Using Deep Learning","volume":"9","author":"Li","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"10.1016\/j.compmedimag.2025.102539_b47","doi-asserted-by":"crossref","DOI":"10.1109\/TBME.2024.3393453","article-title":"Dual-spatial domain generalization for fundus lesion segmentation in unseen manufacturer\u2019s OCT images","author":"Liao","year":"2024","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.compmedimag.2025.102539_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108458","article-title":"Semantic uncertainty guided cross-transformer for enhanced macular edema segmentation in OCT images","volume":"174","author":"Liu","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compmedimag.2025.102539_b49","first-page":"1","article-title":"Boundary-repairing dual-path network for retinal layer segmentation in OCT image with pigment epithelial detachment","author":"Liu","year":"2024","journal-title":"J. Imaging Informatics Med."},{"key":"10.1016\/j.compmedimag.2025.102539_b50","series-title":"2021 IEEE 18th International Symposium on Biomedical Imaging","first-page":"1453","article-title":"Uncertainty-aware semi-supervised framework for automatic segmentation of macular edema in OCT images","author":"Liu","year":"2021"},{"key":"10.1016\/j.compmedimag.2025.102539_b51","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","first-page":"108","article-title":"Simultaneous alignment and surface regression using hybrid 2D-3D networks for 3D coherent layer segmentation of retina OCT images","author":"Liu","year":"2021"},{"key":"10.1016\/j.compmedimag.2025.102539_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.103019","article-title":"Simultaneous alignment and surface regression using hybrid 2D\u20133D networks for 3D coherent layer segmentation of retinal OCT images with full and sparse annotations","volume":"91","author":"Liu","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2025.102539_b53","series-title":"2015 IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"issue":"1\u20132","key":"10.1016\/j.compmedimag.2025.102539_b54","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jns.2009.08.029","article-title":"Essential tremor as a neuropsychiatric disorder","volume":"289","author":"Louis","year":"2010","journal-title":"J. Neurol. Sci."},{"key":"10.1016\/j.compmedimag.2025.102539_b55","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.media.2019.02.011","article-title":"Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network","volume":"54","author":"Lu","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2025.102539_b56","series-title":"2022 International Conference on Robotics and Automation","first-page":"8483","article-title":"Multiple consistency supervision based semi-supervised OCT segmentation using very limited annotations","author":"Lu","year":"2022"},{"key":"10.1016\/j.compmedimag.2025.102539_b57","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2023.102199","article-title":"Boundary-enhanced semi-supervised retinal layer segmentation in optical coherence tomography images using fewer labels","volume":"105","author":"Lu","year":"2023","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2025.102539_b58","doi-asserted-by":"crossref","first-page":"3443","DOI":"10.1109\/JBHI.2020.2999588","article-title":"MS-CAM: Multi-scale class activation maps for weakly-supervised segmentation of geographic atrophy lesions in SD-OCT images","volume":"24","author":"Ma","year":"2020","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"issue":"7","key":"10.1016\/j.compmedimag.2025.102539_b59","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0219126","article-title":"A feature agnostic approach for glaucoma detection in OCT volumes","volume":"14","author":"Maetschke","year":"2019","journal-title":"PloS One"},{"key":"10.1016\/j.compmedimag.2025.102539_b60","first-page":"472","article-title":"Comparison of macular OCTs in right and left eyes of normal people","volume":"vol. 9038","author":"Mahmudi","year":"2014"},{"issue":"3\u20134","key":"10.1016\/j.compmedimag.2025.102539_b61","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1080\/00051144.2021.1973298","article-title":"Annotated retinal optical coherence tomography images (AROI) database for joint retinal layer and fluid segmentation","volume":"62","author":"Melin\u0161 C\u010ccak","year":"2021","journal-title":"Automatika: \u010casopis Za Autom. Mjer. Elektron. Ra\u010dunarstvo I Komun."},{"issue":"9541","key":"10.1016\/j.compmedimag.2025.102539_b62","article-title":"Automated retinal layer segmentation using graph-based algorithm incorporating deep-learning-derived information","volume":"10","author":"Mishra","year":"2020","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102539_b63","doi-asserted-by":"crossref","first-page":"8162","DOI":"10.1038\/s41598-023-35230-4","article-title":"Accurate drusen segmentation in optical coherence tomography via order-constrained regression of retinal layer heights","volume":"13","author":"Morelle","year":"2023","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102539_b64","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1186\/s13104-022-06096-y","article-title":"Towards a guideline for evaluation metrics in medical image segmentation","volume":"15","author":"M\u00fcller","year":"2022","journal-title":"BMC Res. Notes"},{"key":"10.1016\/j.compmedimag.2025.102539_b65","series-title":"2019 IEEE 16th International Symposium on Biomedical Imaging","first-page":"1441","article-title":"U2-net: A Bayesian U-net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans","author":"Orlando","year":"2019"},{"key":"10.1016\/j.compmedimag.2025.102539_b66","series-title":"Lecture Notes in Computer Science,Computer Vision \u2013 ACCV 2018 Workshops","first-page":"316","article-title":"OCT segmentation via deep learning: A review of recent work","author":"Pekala","year":"2019"},{"key":"10.1016\/j.compmedimag.2025.102539_b67","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2019.103445","article-title":"Deep learning based retinal OCT segmentation","volume":"114","author":"Pekala","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compmedimag.2025.102539_b68","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1007\/s10278-020-00383-5","article-title":"Extraction of retinal layers through convolution neural network (CNN) in an OCT image for glaucoma diagnosis","volume":"33","author":"Raja","year":"2020","journal-title":"J. Digit. Imaging"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102539_b69","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1038\/s41433-020-01191-5","article-title":"Deep learning in glaucoma with optical coherence tomography: a review","volume":"35","author":"Ran","year":"2021","journal-title":"Eye"},{"issue":"5","key":"10.1016\/j.compmedimag.2025.102539_b70","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1109\/TMI.2022.3228285","article-title":"RetiFluidNet: A self-adaptive and multi-attention deep convolutional network for retinal OCT fluid segmentation","volume":"42","author":"Rasti","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2025.102539_b71","series-title":"Lecture Notes in Computer Science,Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"8","key":"10.1016\/j.compmedimag.2025.102539_b72","doi-asserted-by":"crossref","first-page":"3987","DOI":"10.1364\/BOE.10.003987","article-title":"Multi-phase level set algorithm based on fully convolutional networks (FCN-MLS) for retinal layer segmentation in SD-OCT images with central serous chorioretinopathy (CSC)","volume":"10","author":"Ruan","year":"2019","journal-title":"Biomed. Opt. Express"},{"key":"10.1016\/j.compmedimag.2025.102539_b73","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1007\/s10278-021-00459-w","article-title":"RetFluidNet: Retinal fluid segmentation for SD-OCT images using convolutional neural network","volume":"34","author":"Sappa","year":"2021","journal-title":"J. Digit. Imaging"},{"key":"10.1016\/j.compmedimag.2025.102539_b74","series-title":"Optical Coherence Tomography of Ocular Diseases","author":"Schuman","year":"2024"},{"key":"10.1016\/j.compmedimag.2025.102539_b75","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103104","article-title":"Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection","volume":"93","author":"Seeb\u00f6ck","year":"2024","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102539_b76","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TMI.2019.2919951","article-title":"Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal OCT","volume":"39","author":"Seeb\u00f6ck","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"10.1016\/j.compmedimag.2025.102539_b77","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1109\/TMI.2023.3240757","article-title":"Graph attention U-net for retinal layer surface detection and choroid neovascularization segmentation in OCT images","volume":"42","author":"Shen","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"10.1016\/j.compmedimag.2025.102539_b78","doi-asserted-by":"crossref","first-page":"3828","DOI":"10.1364\/BOE.492680","article-title":"Semi-supervised OCT lesion segmentation via transformation-consistent with uncertainty and self-deep supervision","volume":"14","author":"Shen","year":"2023","journal-title":"Biomed. Opt. Express"},{"issue":"11","key":"10.1016\/j.compmedimag.2025.102539_b79","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/acd223","article-title":"Segmentation of retinal detachment and retinoschisis in OCT images based on complementary multi-class segmentation networks","volume":"68","author":"Shi","year":"2023","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.compmedimag.2025.102539_b80","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-net and its variants for medical image segmentation: A review of theory and applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.compmedimag.2025.102539_b81","series-title":"2023 IEEE 20th International Symposium on Biomedical Imaging","first-page":"1","article-title":"MDT-net: Multi-domain transfer by perceptual supervision for unpaired images in OCT scan","author":"Song","year":"2023"},{"issue":"10","key":"10.1016\/j.compmedimag.2025.102539_b82","doi-asserted-by":"crossref","first-page":"3568","DOI":"10.1364\/BOE.5.003568","article-title":"Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images","volume":"5","author":"Srinivasan","year":"2014","journal-title":"Biomed. Opt. Express"},{"issue":"8","key":"10.1016\/j.compmedimag.2025.102539_b83","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1016\/j.ophtha.2014.02.023","article-title":"Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN\u2022 OCT consensus","volume":"121","author":"Staurenghi","year":"2014","journal-title":"Ophthalmology"},{"key":"10.1016\/j.compmedimag.2025.102539_b84","article-title":"Retinal layer segmentation in OCT images with boundary regression and feature polarization","author":"Tan","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"10.1016\/j.compmedimag.2025.102539_b85","doi-asserted-by":"crossref","first-page":"2432","DOI":"10.1109\/JBHI.2023.3252665","article-title":"LAGAN: Lesion-aware generative adversarial networks for edema area segmentation in SD-OCT images","volume":"27","author":"Tao","year":"2023","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102539_b86","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1093\/ageing\/afj072","article-title":"Essential tremor\u2014the most common movement disorder in older people","volume":"35","author":"Thanvi","year":"2006","journal-title":"Age Ageing"},{"issue":"8","key":"10.1016\/j.compmedimag.2025.102539_b87","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0133908","article-title":"Real-time automatic segmentation of optical coherence tomography volume data of the macular region","volume":"10","author":"Tian","year":"2015","journal-title":"PloS One"},{"issue":"5","key":"10.1016\/j.compmedimag.2025.102539_b88","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1002\/jbio.201500239","article-title":"Performance evaluation of automated segmentation software on optical coherence tomography volume data","volume":"9","author":"Tian","year":"2016","journal-title":"J. Biophotonics"},{"key":"10.1016\/j.compmedimag.2025.102539_b89","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","first-page":"694","article-title":"Retinal layer segmentation reformulated as OCT language processing","author":"Tran","year":"2020"},{"key":"10.1016\/j.compmedimag.2025.102539_b90","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2025.102539_b91","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.neucom.2022.08.021","article-title":"Deep learning in retinal optical coherence tomography (OCT): A comprehensive survey","volume":"507","author":"Viedma","year":"2022","journal-title":"Neurocomputing"},{"issue":"5","key":"10.1016\/j.compmedimag.2025.102539_b92","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1049\/ipr2.12419","article-title":"Medical image segmentation using deep learning: A survey","volume":"16","author":"Wang","year":"2022","journal-title":"IET Image Process."},{"key":"10.1016\/j.compmedimag.2025.102539_b93","doi-asserted-by":"crossref","first-page":"4713","DOI":"10.1364\/BOE.426803","article-title":"Weakly supervised anomaly segmentation in retinal OCT images using an adversarial learning approach","volume":"12 8","author":"Wang","year":"2021","journal-title":"Biomed. Opt. Express"},{"issue":"7","key":"10.1016\/j.compmedimag.2025.102539_b94","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1109\/TBME.2023.3234031","article-title":"Self-guided optimization semi-supervised method for joint segmentation of macular hole and cystoid macular edema in retinal OCT images","volume":"70","author":"Wang","year":"2023","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.compmedimag.2025.102539_b95","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2024.102837","article-title":"PGKD-Net: Prior-guided and knowledge diffusive network for choroid segmentation","volume":"150","author":"Wang","year":"2024","journal-title":"Artif. Intell. Med."},{"issue":"2","key":"10.1016\/j.compmedimag.2025.102539_b96","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/TMI.2021.3112716","article-title":"MsTGANet: Automatic drusen segmentation from retinal OCT images","volume":"41","author":"Wang","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102539_b97","article-title":"Multivendor spectral-domain optical coherence tomography dataset, observer annotation performance evaluation, and standardized evaluation framework for intraretinal cystoid fluid segmentation","volume":"2016","author":"Wu","year":"2016","journal-title":"J. Ophthalmol."},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102539_b98","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1109\/TMI.2022.3142048","article-title":"Multi-scale pathological fluid segmentation in OCT with a novel curvature loss in convolutional neural network","volume":"41","author":"Xing","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102539_b99","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1364\/BOE.416167","article-title":"Weakly supervised serous retinal detachment segmentation in SD-OCT images by two-stage learning","volume":"12","author":"Xing","year":"2021","journal-title":"Biomed. Opt. Express"},{"key":"10.1016\/j.compmedimag.2025.102539_b100","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108386","article-title":"Choroidal layer analysis in OCT images via ambiguous boundary-aware attention","volume":"175","author":"Yan","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"7","key":"10.1016\/j.compmedimag.2025.102539_b101","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1364\/OSAC.387102","article-title":"RMPPNet: residual multiple pyramid pooling network for subretinal fluid segmentation in SD-OCT images","volume":"3","author":"Yang","year":"2020","journal-title":"OSA Contin."},{"key":"10.1016\/j.compmedimag.2025.102539_b102","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103139","article-title":"Anomaly-guided weakly supervised lesion segmentation on retinal oct images","volume":"94","author":"Yang","year":"2024","journal-title":"Med. Image Anal."},{"issue":"8","key":"10.1016\/j.compmedimag.2025.102539_b103","doi-asserted-by":"crossref","first-page":"3872","DOI":"10.1109\/JBHI.2022.3166778","article-title":"Self-supervised sequence recovery for semi-supervised retinal layer segmentation","volume":"26","author":"Yang","year":"2022","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"issue":"4","key":"10.1016\/j.compmedimag.2025.102539_b104","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/TBME.2021.3115552","article-title":"Joint segmentation of multi-class hyper-reflective foci in retinal optical coherence tomography images","volume":"69","author":"Yao","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.compmedimag.2025.102539_b105","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022","first-page":"667","article-title":"Multiscale unsupervised Retinal Edema Area segmentation in OCT images","author":"Yuan","year":"2022"},{"issue":"12","key":"10.1016\/j.compmedimag.2025.102539_b106","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.3390\/medicina59122114","article-title":"Optical coherence tomography (OCT): a brief look at the uses and technological evolution of ophthalmology","volume":"59","author":"Zeppieri","year":"2023","journal-title":"Medicina"},{"issue":"12","key":"10.1016\/j.compmedimag.2025.102539_b107","doi-asserted-by":"crossref","first-page":"5586","DOI":"10.1109\/TKDE.2021.3070203","article-title":"A survey on multi-task learning","volume":"34","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"7","key":"10.1016\/j.compmedimag.2025.102539_b108","doi-asserted-by":"crossref","first-page":"3294","DOI":"10.1364\/BOE.486276","article-title":"Annotation-efficient learning for OCT segmentation","volume":"14","author":"Zhang","year":"2023","journal-title":"Biomed. Opt. Express"},{"issue":"12","key":"10.1016\/j.compmedimag.2025.102539_b109","doi-asserted-by":"crossref","first-page":"3408","DOI":"10.1109\/JBHI.2020.3023144","article-title":"Automatic segmentation and visualization of choroid in OCT with knowledge infused deep learning","volume":"24","author":"Zhang","year":"2020","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"key":"10.1016\/j.compmedimag.2025.102539_b110","series-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2023","first-page":"570","article-title":"Prior-SSL: A thickness distribution prior and uncertainty guided semi-supervised learning method for choroidal segmentation in OCT images","author":"Zhang","year":"2023"},{"key":"10.1016\/j.compmedimag.2025.102539_b111","doi-asserted-by":"crossref","first-page":"4259","DOI":"10.1007\/s10462-019-09792-7","article-title":"A survey of semi-and weakly supervised semantic segmentation of images","volume":"53","author":"Zhang","year":"2020","journal-title":"Artif. Intell. Rev."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102539_b112","doi-asserted-by":"crossref","DOI":"10.1016\/j.metrad.2023.100003","article-title":"A review of uncertainty estimation and its application in medical imaging","volume":"1","author":"Zou","year":"2023","journal-title":"Meta- Radiol."}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611125000485?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611125000485?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T18:41:19Z","timestamp":1748630479000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611125000485"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":112,"alternative-id":["S0895611125000485"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102539","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Retinal OCT image segmentation with deep learning: A review of advances, datasets, and evaluation metrics","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102539","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102539"}}