{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T12:39:06Z","timestamp":1773232746569,"version":"3.50.1"},"reference-count":90,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Age-related ocular conditions like macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma are leading causes of irreversible vision loss globally. Optical coherence tomography (OCT) provides essential non-invasive visualization of retinal structures for early diagnosis, but manual analysis of these images is labor-intensive and prone to variability. Deep learning (DL) techniques have emerged as powerful tools for automating the segmentation of the retinal layer in OCT scans, potentially improving diagnostic efficiency and consistency. This review systematically evaluates the state of the art in DL-based retinal layer segmentation using the PRISMA methodology. We analyze various architectures (including CNNs, U-Net variants, GANs, and transformers), examine the characteristics and availability of datasets, discuss common preprocessing and data augmentation strategies, identify frequently targeted retinal layers, and compare performance evaluation metrics across studies. Our synthesis highlights significant progress, particularly with U-Net-based models, which often achieve Dice scores exceeding 0.90 for well-defined layers, such as the retinal pigment epithelium (RPE). However, it also identifies ongoing challenges, including dataset heterogeneity, inconsistent evaluation protocols, difficulties in segmenting specific layers (e.g., OPL, RNFL), and the need for improved clinical integration. This review provides a comprehensive overview of current strengths, limitations, and future directions to guide research towards more robust and clinically applicable automated segmentation tools for enhanced ocular disease diagnosis.<\/jats:p>","DOI":"10.3390\/computers14080298","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T11:44:45Z","timestamp":1753184685000},"page":"298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning Techniques for Retinal Layer Segmentation to Aid Ocular Disease Diagnosis: A Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2566-1756","authenticated-orcid":false,"given":"Oliver Jonathan","family":"Quintana-Quintana","sequence":"first","affiliation":[{"name":"Engineering Faculty, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76010, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5455-0329","authenticated-orcid":false,"given":"Marco Antonio","family":"Aceves-Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Engineering Faculty, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76010, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5125-8907","authenticated-orcid":false,"given":"Jes\u00fas Carlos","family":"Pedraza-Ortega","sequence":"additional","affiliation":[{"name":"Engineering Faculty, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76010, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0315-1133","authenticated-orcid":false,"given":"Gendry","family":"Alfonso-Francia","sequence":"additional","affiliation":[{"name":"Engineering Faculty, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76010, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2695-1934","authenticated-orcid":false,"given":"Saul","family":"Tovar-Arriaga","sequence":"additional","affiliation":[{"name":"Engineering Faculty, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76010, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"ref_1","first-page":"844","article-title":"Global data on visual impairment in the year 2002","volume":"82","author":"Resnikoff","year":"2004","journal-title":"Bull. World Health Organ."},{"key":"ref_2","unstructured":"Shaarawy, T.M., Sherwood, M.B., Hitchings, R.A., and Crowston, J.G. (2015). Glaucoma, Elsevier Health Sciences. [2nd ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1016\/j.ophtha.2014.05.013","article-title":"Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040","volume":"121","author":"Tham","year":"2014","journal-title":"Ophthalmology"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cifuentes-Canorea, P., Ruiz-Medrano, J., Gutierrez-Bonet, R., Pe\u00f1a-Garcia, P., Saenz-Frances, F., Garcia-Feijoo, J., and Martinez-de-la Casa, J.M. (2018). Analysis of inner and outer retinal layers using spectral domain optical coherence tomography automated segmentation software in ocular hypertensive and glaucoma patients. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196112"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1136\/bmj.328.7431.97","article-title":"Glaucoma\u20141: Diagnosis","volume":"328","author":"Khaw","year":"2004","journal-title":"BMJ"},{"key":"ref_6","first-page":"1","article-title":"Primary open-angle glaucoma","volume":"2","author":"Weinreb","year":"2016","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1056\/NEJMra0804630","article-title":"Primary open-angle glaucoma","volume":"360","author":"Kwon","year":"2009","journal-title":"N. Engl. J. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1016\/S0140-6736(04)16257-0","article-title":"Primary open-angle glaucoma","volume":"363","author":"Weinreb","year":"2004","journal-title":"Lancet"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1038\/eye.1999.168","article-title":"Acute angle closure glaucoma: An evaluation of a protocol for acute treatment","volume":"13","author":"Choong","year":"1999","journal-title":"Eye"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1016\/S0140-6736(12)60282-7","article-title":"Age-related macular degeneration","volume":"379","author":"Lim","year":"2012","journal-title":"Lancet"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"s84","DOI":"10.2337\/diacare.27.2007.S84","article-title":"Retinopathy in Diabetes","volume":"27","author":"Fong","year":"2004","journal-title":"Diabetes Care"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Drexler, W., and Fujimoto, J.G. (2015). Optical Coherence Tomography: Technology and Applications, Springer International Publishing. [2nd ed.].","DOI":"10.1007\/978-3-319-06419-2"},{"key":"ref_13","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"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4102\/aveh.v75i1.330","article-title":"A review of the human retina with emphasis on nerve fibre layer and macula thicknesses","volume":"75","author":"Mashige","year":"2016","journal-title":"Afr. Vis. Eye Health"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hildebrand, G.D., and Fielder, A.R. (2010). Anatomy and physiology of the retina. Pediatric Retina, Springer.","DOI":"10.1007\/978-3-642-12041-1_2"},{"key":"ref_16","unstructured":"Nguyen, K., Patel, B., and Tadi, P. (2025). Anatomy, Head and Neck: Eye Retina. StatPearls [Internet], StatPearls Publishing."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1167\/iovs.62.9.22","article-title":"Composition of the inner nuclear layer in human retina","volume":"62","author":"Masri","year":"2021","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, S.Y., Park, C.H., Moon, B.H., and Seabold, G.K. (2024). Murine Retina Outer Plexiform Layer Development and Transcriptome Analysis of Pre-Synapses in Photoreceptors. Life, 14.","DOI":"10.3390\/life14091103"},{"key":"ref_19","unstructured":"Mahabadi, N., and Al Khalili, Y. (2025). Neuroanatomy, Retina. StatPearls [Internet], StatPearls Publishing."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1097\/IAE.0000000000000527","article-title":"Directional optical coherence tomography provides accurate outer nuclear layer and Henle fiber layer measurements","volume":"35","author":"Lujan","year":"2015","journal-title":"Retina"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/eye.2011.237","article-title":"External limiting membrane and visual outcome in macular hole repair: Spectral domain OCT analysis","volume":"26","author":"Landa","year":"2012","journal-title":"Eye"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40942-021-00284-x","article-title":"External limiting membrane: Retinal structural barrier in diabetic macular edema","volume":"7","author":"Saxena","year":"2021","journal-title":"Int. J. Retin. Vitr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8042","DOI":"10.1167\/iovs.12-10083","article-title":"Quantification of external limiting membrane disruption caused by diabetic macular edema from SD-OCT","volume":"53","author":"Chen","year":"2012","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1111\/ceo.12952","article-title":"Glucose metabolism in mammalian photoreceptor inner and outer segments","volume":"45","author":"Narayan","year":"2017","journal-title":"Clin. Exp. Ophthalmol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, S., Zhou, J., and Li, D. (2021). Functions and diseases of the retinal pigment epithelium. Front. Pharmacol., 12.","DOI":"10.3389\/fphar.2021.727870"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.media.2016.06.001","article-title":"Quantitative analysis of retinal OCT","volume":"33","author":"Sonka","year":"2016","journal-title":"Med. Image Anal."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1167\/iovs.08-3335","article-title":"Comparison of Cirrus OCT and Stratus OCT on the Ability to Detect Localized Retinal Nerve Fiber Layer Defects in Preperimetric Glaucoma","volume":"51","author":"Jeoung","year":"2010","journal-title":"Investig. Opthalmology Vis. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1016\/j.ajo.2014.08.040","article-title":"Asymmetry Analysis of Macular Inner Retinal Layers for Glaucoma Diagnosis","volume":"158","author":"Yamada","year":"2014","journal-title":"Am. J. Ophthalmol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. (2018). Neural Networks and Deep Learning: A Textbook, Springer International Publishing.","DOI":"10.1007\/978-3-319-94463-0"},{"key":"ref_31","unstructured":"Bengio, Y., Goodfellow, I., and Courville, A. (2017). Deep Learning, MIT Press."},{"key":"ref_32","unstructured":"Charniak, E. (2019). Introduction to Deep Learning, Cambridge University Press."},{"key":"ref_33","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":"2017","journal-title":"Commun. ACM"},{"key":"ref_34","unstructured":"Ronneberger, O., 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. proceedings, part III 18."},{"key":"ref_35","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., and Weinberger, K. (2014). Generative Adversarial Nets. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_37","unstructured":"Vaswani, A. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_38","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Dong, X., Bao, J., Chen, D., Zhang, W., Yu, N., Yuan, L., Chen, D., and Guo, B. (2022, January 18\u201324). CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01181"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105342","DOI":"10.1016\/j.dib.2020.105342","article-title":"Data on OCT and fundus images for the detection of glaucoma","volume":"29","author":"Raja","year":"2020","journal-title":"Data Brief"},{"key":"ref_43","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":"ref_44","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":"ref_45","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":"ref_46","doi-asserted-by":"crossref","unstructured":"Chiu, S.J., Allingham, M.J., Mettu, P.S., Cousins, S.W., Izatt, J.A., and Farsiu, S. (2015). Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema. Biomed. Opt. Express, 6.","DOI":"10.1364\/BOE.6.001172"},{"key":"ref_47","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":"Vatavuk","year":"2021","journal-title":"Automatika"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"106532","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":"ref_49","doi-asserted-by":"crossref","unstructured":"He, X., Wang, Y., Poiesi, F., Song, W., Xu, Q., Feng, Z., and Wan, Y. (2023). Exploiting multi-granularity visual features for retinal layer segmentation in human eyes. Front. Bioeng. Biotechnol., 11.","DOI":"10.3389\/fbioe.2023.1191803"},{"key":"ref_50","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. Opthalmology Vis. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4509","DOI":"10.1364\/BOE.9.004509","article-title":"Multiple surface segmentation using convolution neural nets: Application to retinal layer segmentation in OCT images","volume":"9","author":"Shah","year":"2018","journal-title":"Biomed. Opt. Express"},{"key":"ref_52","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"},{"key":"ref_53","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. Health Inform."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.neucom.2019.07.079","article-title":"Automatic segmentation of retinal layer boundaries in OCT images using multiscale convolutional neural network and graph search","volume":"365","author":"Hu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_55","first-page":"3198657","article-title":"Multiscale Attention Gated Network (MAGNet) for Retinal Layer and Macular Cystoid Edema Segmentation","volume":"10","author":"Cruz","year":"2022","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1364\/BOE.10.002639","article-title":"Joint retina segmentation and classification for early glaucoma diagnosis","volume":"10","author":"Wang","year":"2019","journal-title":"Biomed. Opt. Express"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"101856","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":"ref_58","doi-asserted-by":"crossref","unstructured":"Fazekas, B., Aresta, G., Lachinov, D., Riedl, S., Mai, J., Schmidt-Erfurth, U., and Bogunovi\u0107, H. (2022). SD-LayerNet: Semi-supervised Retinal Layer Segmentation in OCT Using Disentangled Representation with Anatomical Priors. Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022, Springer.","DOI":"10.1007\/978-3-031-16452-1_31"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Cao, J., Liu, X., Zhang, Y., and Wang, M. (2020, January 11\u201314). A Multi-task Framework for Topology-guaranteed Retinal Layer Segmentation in OCT Images. Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada.","DOI":"10.1109\/SMC42975.2020.9283408"},{"key":"ref_60","unstructured":"V\u00e1zquez, E.R., Rodr\u00edguez, M.N.B., L\u00f3pez-Varela, E., and Penedo, M.G. (2023, January 18\u201320). Deep Learning for Segmentation of Optic Disc and Retinal Layers in Peripapillary Optical Coherence Tomography Images. Proceedings of the Fifteenth International Conference on Machine Vision (ICMV 2022), Rome, Italy."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Matovinovic, I.Z., Loncaric, S., Lo, J., Heisler, M., and Sarunic, M. (2019, January 23\u201325). Transfer learning with U-net type model for automatic segmentation of three retinal layers in optical coherence tomography images. Proceedings of the 2019 11th International Symposium on Image and Signal Processing and Analysis, Dubrovnik, Croatia.","DOI":"10.1109\/ISPA.2019.8868639"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kepp, T., Ehrhardt, J., Heinrich, M.P., H\u00fcttmann, G., and Handels, H. (2019, January 8\u201311). Topology-Preserving Shape-Based Regression Of Retinal Layers In Oct Image Data Using Convolutional Neural Networks. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759261"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1364\/BOE.516045","article-title":"Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve","volume":"15","author":"Chen","year":"2024","journal-title":"Biomed. Opt. Express"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ndipenoch, N., Miron, A., Wang, Z., and Li, Y. (2022, January 5\u20137). Simultaneous Segmentation of Layers and Fluids in Retinal OCT Images. Proceedings of the 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Beijing, China.","DOI":"10.1109\/CISP-BMEI56279.2022.9979957"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Moradi, M., Chen, Y., Du, X., and Seddon, J.M. (2023). Deep ensemble learning for automated non-advanced AMD classification using optimized retinal layer segmentation and SD-OCT scans. Comput. Biol. Med., 154.","DOI":"10.1016\/j.compbiomed.2022.106512"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhilin, Z., Yan, W., Zeyu, P., Yunqing, Z., Rugang, Y., and Guogang, C. (2023, January 23\u201325). Dual Attention Network for Retinal Layer and Fluid Segmentation in OCT. Proceedings of the 2023 8th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan.","DOI":"10.1109\/ICIIBMS60103.2023.10347833"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3195","DOI":"10.1364\/BOE.450193","article-title":"Retinal layer segmentation in optical coherence tomography (OCT) using a 3D deep-convolutional regression network for patients with age-related macular degeneration","volume":"13","author":"Mukherjee","year":"2022","journal-title":"Biomed. Opt. Express"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3190","DOI":"10.1364\/BOE.492670","article-title":"Deep learning network with differentiable dynamic programming for retina OCT surface segmentation","volume":"14","author":"Xie","year":"2023","journal-title":"Biomed. Opt. Express"},{"key":"ref_69","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"},{"key":"ref_70","first-page":"1","article-title":"Embedded Residual Recurrent Network and Graph Search for the Segmentation of Retinal Layer Boundaries in Optical Coherence Tomography","volume":"70","author":"Hu","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"5759","DOI":"10.1364\/BOE.9.005759","article-title":"Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search","volume":"9","author":"Kugelman","year":"2018","journal-title":"Biomed. Opt. Express"},{"key":"ref_72","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":"ref_73","doi-asserted-by":"crossref","unstructured":"Mishra, Z., Ganegoda, A., Selicha, J., Wang, Z., Sadda, S.R., and Hu, Z. (2020). Automated Retinal Layer Segmentation Using Graph-based Algorithm Incorporating Deep-learning-derived Information. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-66355-5"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Li, J., Ling, Y., He, J., Jin, P., Zhu, J., Zou, H., Xu, X., Shao, S., Gan, Y., and Su, Y. (2021, January 6\u201311). A GCN-assisted deep learning method for peripapillary retinal layer segmentation in OCT images. Proceedings of the Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV, Online.","DOI":"10.1117\/12.2582905"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"2732","DOI":"10.1364\/BOE.8.002732","article-title":"Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search","volume":"8","author":"Fang","year":"2017","journal-title":"Biomed. Opt. Express"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"103319","DOI":"10.1109\/ACCESS.2023.3317011","article-title":"Retinal OCT Layer Segmentation via Joint Motion Correction and Graph-Assisted 3D Neural Network","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"3843","DOI":"10.1364\/BOE.392648","article-title":"Semi-supervised deep learning based 3D analysis of the peripapillary region","volume":"11","author":"Heisler","year":"2020","journal-title":"Biomed. Opt. Express"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Xue, S., Wang, H., Guo, X., Sun, M., Song, K., Shao, Y., Zhang, H., and Zhang, T. (2023). CTS-Net: A Segmentation Network for Glaucoma Optical Coherence Tomography Retinal Layer Images. Bioengineering, 10.","DOI":"10.3390\/bioengineering10020230"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"100919","DOI":"10.1016\/j.preteyeres.2020.100919","article-title":"Photoreceptor cells and RPE contribute to the development of diabetic retinopathy","volume":"83","author":"Tonade","year":"2021","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_80","first-page":"95","article-title":"Correlating RNFL thickness by OCT with perimetric sensitivity in glaucoma patients","volume":"21","author":"Wheat","year":"2012","journal-title":"J. Glaucoma"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Morelle, O., Wintergerst, M.W., Finger, R.P., and Schultz, T. (2023). Accurate drusen segmentation in optical coherence tomography via order-constrained regression of retinal layer heights. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-35230-4"},{"key":"ref_82","unstructured":"(2024, December 18). Baidu AI Studio Competition: Retinal OCT Layer Segmentation. Available online: https:\/\/aistudio.baidu.com\/aistudio\/competition\/detail\/783\/0\/introduction."},{"key":"ref_83","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":"ref_84","doi-asserted-by":"crossref","unstructured":"Sousa, J.A., Paiva, A., Silva, A., Almeida, J.D., Braz, J.G., Diniz, J.O., Figueredo, W.K., and Gattass, M. (2021). Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0251591"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"2846","DOI":"10.21037\/qims-22-959","article-title":"Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning","volume":"13","author":"Gende","year":"2023","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s10916-019-1452-9","article-title":"Automatic annotation of retinal layers in optical coherence tomography images","volume":"43","author":"Dodo","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Konno, T., Ninomiya, T., Miura, K., Ito, K., Himori, N., Sharma, P., Nakazawa, T., and Aoki, T. (2023, January 18\u201321). Retinal Layer Segmentation from Oct Images Using 2D-3D Hybrid Network with Multi-Scale Loss and Refinement Module. Proceedings of the 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia.","DOI":"10.1109\/ISBI53787.2023.10230693"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Hassan, T., Usman, A., Akram, M.U., Masood, M.F., and Yasin, U. (2018, January 17\u201320). Deep Learning Based Automated Extraction of Intra-Retinal Layers for Analyzing Retinal Abnormalities. Proceedings of the 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), Ostrava, Czech Republic.","DOI":"10.1109\/HealthCom.2018.8531198"},{"key":"ref_89","first-page":"5005612","article-title":"Confidence-Guided Topology-Preserving Layer Segmentation for Optical Coherence Tomography Images with Focus-Column Module","volume":"70","author":"Liu","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"60929","DOI":"10.1109\/ACCESS.2020.2983818","article-title":"The Segmentation of Retinal Layer and Fluid in SD-OCT Images Using Mutex Dice Loss Based Fully Convolutional Networks","volume":"8","author":"Wei","year":"2020","journal-title":"IEEE Access"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/8\/298\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:13:48Z","timestamp":1760033628000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/8\/298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,22]]},"references-count":90,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["computers14080298"],"URL":"https:\/\/doi.org\/10.3390\/computers14080298","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,22]]}}}