{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:52:58Z","timestamp":1765486378400,"version":"3.44.0"},"reference-count":105,"publisher":"Elsevier","isbn-type":[{"type":"print","value":"9780081028162"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1016\/b978-0-08-102816-2.00019-8","type":"book-chapter","created":{"date-parts":[[2019,11,22]],"date-time":"2019-11-22T10:25:10Z","timestamp":1574418310000},"page":"379-404","source":"Crossref","is-referenced-by-count":7,"title":["Artificial intelligence and deep learning in retinal image analysis"],"prefix":"10.1016","author":[{"given":"Philippe","family":"Burlina","sequence":"first","affiliation":[]},{"given":"Adrian","family":"Galdran","sequence":"additional","affiliation":[]},{"given":"Pedro","family":"Costa","sequence":"additional","affiliation":[]},{"given":"Adam","family":"Cohen","sequence":"additional","affiliation":[]},{"given":"Aur\u00e9lio","family":"Campilho","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"15","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0010","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.1001\/jama.291.15.1900","article-title":"Age-related macular degeneration is the leading cause of blindness","volume":"291","author":"Bressler","year":"2004","journal-title":"JAMA"},{"issue":"3","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0015","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1097\/IAE.0000000000000036","article-title":"Current knowledge and trends in age-related macular degeneration: genetics, epidemiology, and prevention","volume":"34","author":"Velez-Montoya","year":"2014","journal-title":"Retina"},{"issue":"1","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0020","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s40662-015-0026-2","article-title":"Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss","volume":"2","author":"Lee","year":"2015","journal-title":"Eye Vis."},{"issue":"14","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0025","doi-asserted-by":"crossref","first-page":"ORSF5","DOI":"10.1167\/iovs.13-12789","article-title":"The prevalence of age-related eye diseases and visual impairment in aging: current estimates","volume":"54","author":"Klein","year":"2013","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"issue":"5","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0030","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0039-6257(05)80092-X","article-title":"An international classification and grading system for age-related maculopathy and age-related macular degeneration","volume":"39","author":"Bird","year":"1995","journal-title":"Surv. Ophthalmol."},{"issue":"11","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0035","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1001\/archopht.121.11.1621","article-title":"Potential public health impact of age-related eye disease study results: {AREDS} Report No. 11","volume":"121","year":"2003","journal-title":"Arch. Ophthalmol."},{"issue":"10","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0040","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1001\/archopht.119.10.1417","article-title":"A randomized, placebo-controlled, clinical trial of high-dose supplementation with vitamins {C} and {E}, beta carotene, and zinc for age-related macular degeneration and vision loss: {AREDS} Report No. 8","volume":"119","year":"2001","journal-title":"Arch. Ophthalmol."},{"issue":"3","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0045","first-page":"145","article-title":"Pathways to neurodegeneration: mechanistic insights from GWAS in Alzheimer\u2019s disease, Parkinson\u2019s disease, and related disorders","volume":"2","author":"Ramanan","year":"2013","journal-title":"Am. J. Neurodegener. Dis."},{"issue":"2","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0050","doi-asserted-by":"crossref","first-page":"164","DOI":"10.2174\/157015908784533851","article-title":"Neurodegenerative diseases of the retina and potential for protection and recovery","volume":"6","author":"Schmidt","year":"2008","journal-title":"Curr. Neuropharmacol."},{"issue":"2","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0055","doi-asserted-by":"crossref","first-page":"e0192646","DOI":"10.1371\/journal.pone.0192646","article-title":"Evaluation of inner retinal layers as biomarkers in mild cognitive impairment to moderate Alzheimer\u2019s disease","volume":"13","author":"Lad","year":"2018","journal-title":"PLoS ONE"},{"issue":"7639","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0060","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"issue":"11","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0065","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1001\/jamaophthalmol.2017.3782","article-title":"Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks","volume":"135","author":"Burlina","year":"2017","journal-title":"JAMA Ophtalmol."},{"issue":"4","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0070","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.oret.2016.12.009","article-title":"Deep learning is effective for classifying normal versus age-related macular degeneration OCT images","volume":"1","author":"Lee","year":"2017","journal-title":"Ophthalmol. Retina"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0075","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0080","series-title":"in: Deep Learning and Convolutional Neural Networks for Medical Image Computing","first-page":"11","article-title":"Review of deep learning methods in mammography, cardiovascular, and microscopy image analysis","author":"Carneiro","year":"2017"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0085","doi-asserted-by":"crossref","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","article-title":"Deep learning applications in medical image analysis","volume":"6","author":"Ker","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0090","series-title":"Deep Learning for Medical Image Processing: Overview, Challenges and the Future","first-page":"323","author":"Razzak","year":"2018"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0095","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"issue":"3","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0100","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1080\/17469899.2017.1307105","article-title":"A review of feature-based retinal image analysis","volume":"12","author":"Jordan","year":"2017","journal-title":"Expert Rev. Ophthalmol."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0105","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.preteyeres.2018.07.004","article-title":"Artificial intelligence in retina","volume":"67","author":"Schmidt-Erfurth","year":"2018","journal-title":"Prog. Retin. Eye Res."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0110","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1111\/ceo.13381","article-title":"Current state and future prospects of artificial intelligence in ophthalmology: a review","volume":"47","author":"Hogarty","year":"2019","journal-title":"Clin. Exp. Ophthalmol."},{"issue":"4","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0115","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.jcjo.2018.04.019","article-title":"Deep learning in ophthalmology: a review","volume":"53","author":"Grewal","year":"2018","journal-title":"Can. J. Ophthalmol."},{"issue":"2","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0120","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"Br. J. Ophthalmol."},{"issue":"1","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0125","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.ins.2007.07.020","article-title":"Identification of different stages of diabetic retinopathy using retinal optical images","volume":"178","author":"Yun","year":"2008","journal-title":"Inf. Sci."},{"issue":"1","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0130","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cmpb.2008.07.006","article-title":"Neural network based detection of hard exudates in retinal images","volume":"93","author":"Garc\u00eda","year":"2009","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0135","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0140","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0145","first-page":"1175","article-title":"The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation","author":"J\u00e9gou","year":"2017"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0150","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0155","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0160","article-title":"Deep learning based retinal OCT segmentation","author":"Pekala","year":"2018","journal-title":"arXiv preprint arXiv:1801.09749"},{"issue":"4","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0165","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1111\/dme.12119","article-title":"Prevalence of diabetic retinopathy in type 2 diabetes in developing and developed countries","volume":"30","author":"Ruta","year":"2013","journal-title":"Diabet. Med."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0170","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.diabres.2017.03.024","article-title":"IDF diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040","volume":"128","author":"Ogurtsova","year":"2017","journal-title":"Diabetes Res. Clin. Pract."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0175","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.media.2017.04.012","article-title":"Deep image mining for diabetic retinopathy screening","volume":"39","author":"Quellec","year":"2017","journal-title":"Med. Image Anal."},{"issue":"6","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0180","first-page":"649","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"304","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"issue":"13","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0185","doi-asserted-by":"crossref","first-page":"5200","DOI":"10.1167\/iovs.16-19964","article-title":"Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning","volume":"57","author":"Abr\u00e0moff","year":"2016","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0190","series-title":"Advances in Neural Information Processing Systems, vol. 25","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0195","article-title":"Very deep convolutional networks for large-scale image recognition","volume":"abs\/1409.1556","author":"Simonyan","year":"2014","journal-title":"CoRR"},{"issue":"7","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0200","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","article-title":"Automated identification of diabetic retinopathy using deep learning","volume":"124","author":"Gargeya","year":"2017","journal-title":"Ophthalmology"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0205","first-page":"2921","article-title":"Learning deep features for discriminative localization","author":"Zhou","year":"2016"},{"issue":"22","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0210","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0215","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.compbiomed.2017.01.018","article-title":"Comparing humans and deep learning performance for grading {AMD}: a study in using universal deep features and transfer learning for automated {AMD} analysis","volume":"82","author":"Burlina","year":"2017","journal-title":"Comput. Biol. Med."},{"issue":"12","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0220","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1001\/jamaophthalmol.2018.4118","article-title":"Use of deep learning for detailed severity characterization and estimation of 5-year risk among patients with age-related macular degeneration","volume":"136","author":"Burlina","year":"2018","journal-title":"JAMA Ophthalmol."},{"issue":"11","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0225","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1001\/jamaophthalmol.2018.3799","article-title":"Utility of deep learning methods for referability classification of age-related macular degeneration","volume":"136","author":"Burlina","year":"2018","journal-title":"JAMA Ophthalmol."},{"issue":"5","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0230","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/S0002-9394(01)01218-1","article-title":"The Age-Related Eye Disease Study system for classifying age-related macular degeneration from stereoscopic color fundus photographs: the Age-Related Eye Disease Study Report Number 6","volume":"132","year":"2001","journal-title":"Am. J. Ophthalmol."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0235","first-page":"184","article-title":"Detection of age-related macular degeneration via deep learning","author":"Burlina","year":"2016"},{"issue":"5","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0240","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1038\/s41591-018-0029-3","article-title":"AI for medical imaging goes deep","volume":"24","author":"Ting","year":"2018","journal-title":"Nat. Med."},{"issue":"9","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0245","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.1016\/j.ophtha.2018.02.037","article-title":"A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography","volume":"125","author":"Grassmann","year":"2018","journal-title":"Ophthalmology"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0250","first-page":"68","article-title":"Automated retinopathy of prematurity case detection with convolutional neural networks","author":"Worrall","year":"2016"},{"issue":"7","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0255","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1001\/jamaophthalmol.2018.1934","article-title":"Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks","volume":"136","author":"Brown","year":"2018","journal-title":"JAMA Ophthalmol."},{"issue":"1","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0260","first-page":"263","article-title":"Automated analysis for retinopathy of prematurity by deep neural networks","volume":"38","author":"Hu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0265","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/nrneurol.2012.227","article-title":"The retina as a window to the brain from eye research to CNS disorders","volume":"9","author":"London","year":"2013","journal-title":"Nat. Rev. Neurol."},{"issue":"5","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0270","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. Biophoton."},{"article-title":"A review of algorithms for segmentation of retinal image data using optical coherence tomography","year":"2011","author":"DeBuc","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0275"},{"year":"2014","series-title":"Spectralis HRA+OCT User Manual Software","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0280"},{"year":"2014","series-title":"The Iowa Reference Algorithms (Retinal Image Analysis Lab, Iowa Institute for Biomedical Imaging, IA)","author":"Lee","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0285"},{"issue":"7","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0290","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1364\/BOE.4.001133","article-title":"Retinal layer segmentation of macular {OCT} images using boundary classification","volume":"4","author":"Lang","year":"2013","journal-title":"Biomed. Opt. Express"},{"issue":"3","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0295","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TMI.2012.2225152","article-title":"Graph-based multi-surface segmentation of {OCT} data using trained hard and soft constraints","volume":"32","author":"Dufour","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0300","doi-asserted-by":"crossref","first-page":"e0133908","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"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0305","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1038\/eye.2017.61","article-title":"Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images","volume":"31","author":"Breger","year":"2017","journal-title":"Eye"},{"issue":"4","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0310","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"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0315","first-page":"202","article-title":"Towards topological correct segmentation of macular OCT from cascaded FCNs","author":"He","year":"2017"},{"issue":"5","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0320","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"},{"issue":"8","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0325","doi-asserted-by":"crossref","first-page":"3627","DOI":"10.1364\/BOE.8.003627","article-title":"ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks","volume":"8","author":"Roy","year":"2017","journal-title":"Biomed. Opt. Express"},{"issue":"1","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0330","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1167\/iovs.17-22617","article-title":"A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head","volume":"59","author":"Devalla","year":"2018","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"issue":"7","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0335","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1364\/BOE.8.003440","article-title":"Deep-learning based, automated segmentation of macular edema in optical coherence tomography","volume":"8","author":"Lee","year":"2017","journal-title":"Biomed. Opt. Express."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0340","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.cmpb.2018.02.001","article-title":"Blood vessel segmentation algorithms review of methods, datasets and evaluation metrics","volume":"158","author":"Moccia","year":"2018","journal-title":"Comput. Methods Prog. Biomed."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0345","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.bspc.2018.01.014","article-title":"Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma","volume":"42","author":"Thakur","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"issue":"11","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0350","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/TMI.2016.2546227","article-title":"Segmenting retinal blood vessels with deep neural networks","volume":"35","author":"Liskowski","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0355","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TMI.2015.2457891","article-title":"A cross-modality learning approach for vessel segmentation in retinal images","volume":"35","author":"Li","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0360","first-page":"140","article-title":"Deep retinal image understanding","volume":"vol. 9901","author":"Maninis","year":"2016"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0365","series-title":"DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field","first-page":"132","author":"Fu","year":"2016"},{"issue":"12","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0370","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.1007\/s11548-017-1619-0","article-title":"Multi-level deep supervised networks for retinal vessel segmentation","volume":"12","author":"Mo","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"1","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0375","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/TMI.2018.2854886","article-title":"Supervised segmentation of un-annotated retinal fundus images by synthesis","volume":"38","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0380","first-page":"1","article-title":"Automatic retinal vessel segmentation via deeply supervised and smoothly regularized network","author":"Lin","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0385","first-page":"1","article-title":"A three-stage deep learning model for accurate retinal vessel segmentation","author":"Yan","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0390","series-title":"Multiscale Network Followed Network Model for Retinal Vessel Segmentation","first-page":"119","author":"Wu","year":"2018"},{"issue":"9","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0395","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.1109\/TBME.2018.2828137","article-title":"Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation","volume":"65","author":"Yan","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0400","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.compbiomed.2017.09.005","article-title":"Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort","volume":"90","author":"Welikala","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0405","first-page":"622","article-title":"Deep convolutional artery\/vein classification of retinal vessels","volume":"vol. 10882","author":"Meyer","year":"2018"},{"issue":"7","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0410","doi-asserted-by":"crossref","first-page":"3153","DOI":"10.1364\/BOE.9.003153","article-title":"Simultaneous arteriole and venule segmentation with domain-specific loss function on a new public database","volume":"9","author":"Xu","year":"2018","journal-title":"Biomed. Opt. Express"},{"article-title":"Uncertainty-aware retinal vessel classification on retinal images","year":"2019","author":"Galdran","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0415"},{"issue":"7","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0420","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","article-title":"Joint optic disc and cup segmentation based on multi-label deep network and polar transformation","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0425","series-title":"Boosting Convolutional Filters with Entropy Sampling for Optic Cup and Disc Image Segmentation From Fundus Images","first-page":"136","author":"Zilly","year":"2015"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0430","series-title":"DeepDisc: Optic Disc Segmentation Based on Atrous Convolution and Spatial Pyramid Pooling","first-page":"253","author":"Gu","year":"2018"},{"issue":"2","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0435","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1049\/iet-ipr.2018.5922","article-title":"Optic disc segmentation in fundus images using adversarial training","volume":"13","author":"Liu","year":"2018","journal-title":"IET Image Process."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0440","first-page":"5954","article-title":"Optic disc segmentation from retinal fundus images via deep object detection networks","author":"Sun","year":"2018"},{"issue":"11","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0445","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1109\/TMI.2018.2837012","article-title":"Disc-aware ensemble network for glaucoma screening from fundus image","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0450","series-title":"Semi-Supervised Segmentation of Optic Cup in Retinal Fundus Images Using Variational Autoencoder","first-page":"75","author":"Sedai","year":"2017"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0455","first-page":"1083","article-title":"Multi-stage segmentation of the fovea in retinal fundus images using fully convolutional neural networks","author":"Sedai","year":"2017"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0460","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.bspc.2017.09.008","article-title":"Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc","volume":"40","author":"Al-Bander","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0465","first-page":"39","article-title":"A pixel-wise distance regression approach for joint retinal optical disc and fovea detection","volume":"vol. 11071","author":"Meyer","year":"2018"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0470","series-title":"UOLO\u2014Automatic Object Detection and Segmentation in Biomedical Images","first-page":"165","author":"Ara\u00fajo","year":"2018"},{"issue":"3","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0475","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","article-title":"Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning","volume":"2","author":"Poplin","year":"2018","journal-title":"Nat. Biomed. Eng."},{"issue":"3","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0480","doi-asserted-by":"crossref","first-page":"231","DOI":"10.5566\/ias.1155","article-title":"Feedback on a publicly distributed image database: the Messidor database","volume":"33","author":"Decenci\u00e8re","year":"2014","journal-title":"Image Anal. Stereol."},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0485","first-page":"711","article-title":"Diabetic retinopathy image database (DRiDB): a new database for diabetic retinopathy screening programs research","author":"Prentasic","year":"2013"},{"issue":"1","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0490","doi-asserted-by":"crossref","first-page":"014503","DOI":"10.1117\/1.JMI.4.1.014503","article-title":"DR HAGIS\u2014a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients","volume":"4","author":"Holm","year":"2017","journal-title":"J. Med. Imaging (Bellingham, Wash.)"},{"issue":"3","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0495","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3390\/data3030025","article-title":"Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research","volume":"3","author":"Porwal","year":"2018","journal-title":"Data"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0500","first-page":"15.1","article-title":"The DIARETDB1 diabetic retinopathy database and evaluation protocol","author":"Kauppi","year":"2007"},{"issue":"1","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0505","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TMI.2009.2033909","article-title":"Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs","volume":"29","author":"Niemeijer","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0510","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.irbm.2013.01.010","article-title":"TeleOphta: machine learning and image processing methods for teleophthalmology","volume":"34","author":"Decenci\u00e8re","year":"2013","journal-title":"IRBM"},{"issue":"4","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0515","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/TMI.2004.825627","article-title":"Ridge-based vessel segmentation in color images of the retina","volume":"23","author":"Staal","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"10.1016\/B978-0-08-102816-2.00019-8_bb0520","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/42.845178","article-title":"Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response","volume":"19","author":"Hoover","year":"2000","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0525","doi-asserted-by":"crossref","first-page":"154860","DOI":"10.1155\/2013\/154860","article-title":"Robust vessel segmentation in fundus images","volume":"2013","author":"Budai","year":"2013","journal-title":"Int. J. Biomed. Imaging"},{"key":"10.1016\/B978-0-08-102816-2.00019-8_bb0530","first-page":"65","article-title":"Towards a glaucoma risk index based on simulated hemodynamics from fundus images","author":"Orlando","year":"2018"}],"container-title":["Computational Retinal Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:B9780081028162000198?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:B9780081028162000198?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T12:15:21Z","timestamp":1757679321000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/B9780081028162000198"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9780081028162"],"references-count":105,"URL":"https:\/\/doi.org\/10.1016\/b978-0-08-102816-2.00019-8","relation":{},"subject":[],"published":{"date-parts":[[2019]]}}}