{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T11:23:33Z","timestamp":1774351413439,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T00:00:00Z","timestamp":1615334400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T00:00:00Z","timestamp":1615334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Rebecca L. Cooper 2018 Project Grant"},{"name":"Australian National Health and Medical Research Council Fellowship","award":["MRF1142962"],"award-info":[{"award-number":["MRF1142962"]}]},{"name":"Telethon-Perth Children\u2019s Hospital Research Fund"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s00521-021-05826-w","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T09:05:39Z","timestamp":1615367139000},"page":"7393-7408","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Data augmentation for patch-based OCT chorio-retinal segmentation using generative adversarial networks"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7269-8573","authenticated-orcid":false,"given":"Jason","family":"Kugelman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7754-6592","authenticated-orcid":false,"given":"David","family":"Alonso-Caneiro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1595-673X","authenticated-orcid":false,"given":"Scott A.","family":"Read","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5998-1320","authenticated-orcid":false,"given":"Stephen J.","family":"Vincent","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2809-9930","authenticated-orcid":false,"given":"Fred K.","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5226-5498","authenticated-orcid":false,"given":"Michael J.","family":"Collins","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,10]]},"reference":[{"issue":"4","key":"5826_CR1","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1016\/j.ajo.2011.03.008","volume":"152","author":"V Manjunath","year":"2011","unstructured":"Manjunath V, Goren J, Fujimoto JG, Duker JS (2011) Analysis of choroidal thickness in age-related macular degeneration using spectral-domain optical coherence tomography. Am J Ophthalmol 152(4):663\u2013668","journal-title":"Am J Ophthalmol"},{"issue":"8","key":"5826_CR2","doi-asserted-by":"publisher","first-page":"5311","DOI":"10.1167\/iovs.10-6875","volume":"52","author":"M Esmaeelpour","year":"2011","unstructured":"Esmaeelpour M et al (2011) Mapping choroidal and retinal thickness variation in type 2 diabetes using three-dimensional 1060-nm optical coherence tomography. Invest Ophthalmol Vis Sci 52(8):5311\u20135316","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"4","key":"5826_CR3","doi-asserted-by":"publisher","first-page":"2893","DOI":"10.1167\/iovs.12-11085","volume":"54","author":"DA Sim","year":"2013","unstructured":"Sim DA et al (2013) Repeatability and reproducibility of choroidal vessel layer measurements in diabetic retinopathy using enhanced depth optical coherence tomography. Invest Ophthalmol Vis Sci 54(4):2893\u20132901","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"9","key":"5826_CR4","doi-asserted-by":"publisher","first-page":"900","DOI":"10.1109\/42.952728","volume":"20","author":"D Koozekanani","year":"2001","unstructured":"Koozekanani D, Boyer K, Roberts C (2001) Retinal thickness measurements from optical coherence tomography using a Markov boundary model. IEEE Trans Med Imaging 20(9):900\u2013916","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"5826_CR5","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1364\/BOE.8.000281","volume":"8","author":"J Oliveira","year":"2017","unstructured":"Oliveira J, Pereira S, Gon\u00e7alves L, Ferreira M, Silva CA (2017) Multi-surface segmentation of OCT images with AMD using sparse high order potentials. Biomed Opt Express 8(1):281\u2013297","journal-title":"Biomed Opt Express"},{"issue":"25","key":"5826_CR6","doi-asserted-by":"publisher","first-page":"10200","DOI":"10.1364\/OPEX.13.010200","volume":"13","author":"D Cabrera Fern\u00e1ndez","year":"2005","unstructured":"Cabrera Fern\u00e1ndez D, Salinas HM, Puliafito CA (2005) Automated detection of retinal layer structures on optical coherence tomography images. Opt Express 13(25):10200\u201310216","journal-title":"Opt Express"},{"issue":"18","key":"5826_CR7","doi-asserted-by":"publisher","first-page":"19413","DOI":"10.1364\/OE.18.019413","volume":"18","author":"SJ Chiu","year":"2010","unstructured":"Chiu SJ et al (2010) Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation. Opt Express 18(18):19413\u201319428","journal-title":"Opt Express"},{"issue":"2","key":"5826_CR8","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1364\/BOE.7.000581","volume":"7","author":"S Niu","year":"2016","unstructured":"Niu S, de Sisternes L, Chen Q, Leng T, Rubin DL (2016) Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor. Biomed Opt Express 7(2):581\u2013600","journal-title":"Biomed Opt Express"},{"issue":"4","key":"5826_CR9","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1364\/BOE.6.001172","volume":"6","author":"SJ Chiu","year":"2015","unstructured":"Chiu SJ et al (2015) Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema. Biomed Opt Express 6(4):1172\u20131194","journal-title":"Biomed Opt Express"},{"issue":"2","key":"5826_CR10","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1364\/BOE.5.000348","volume":"5","author":"PP Srinivasan","year":"2014","unstructured":"Srinivasan PP, Heflin SJ, Izatt JA, Arshavsky VY, Farsiu S (2014) Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology. Biomed Opt Express 5(2):348\u2013365","journal-title":"Biomed Opt Express"},{"issue":"7","key":"5826_CR11","doi-asserted-by":"publisher","first-page":"2888","DOI":"10.1364\/BOE.7.002888","volume":"7","author":"SP Karri","year":"2016","unstructured":"Karri SP, Chakraborthi D, Chatterjee J (2016) Learning layer-specific edges for segmenting retinal layers with large deformations. Biomed Opt Express 7(7):2888\u20132901","journal-title":"Biomed Opt Express"},{"issue":"5","key":"5826_CR12","doi-asserted-by":"publisher","first-page":"2732","DOI":"10.1364\/BOE.8.002732","volume":"8","author":"L Fang","year":"2017","unstructured":"Fang L et al (2017) Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed Opt Express 8(5):2732\u20132744","journal-title":"Biomed Opt Express"},{"issue":"7","key":"5826_CR13","doi-asserted-by":"publisher","first-page":"3049","DOI":"10.1364\/BOE.9.003049","volume":"9","author":"J Hamwood","year":"2018","unstructured":"Hamwood J, Alonso-Caneiro D, Read SA, Vincent SJ, Collins MJ (2018) Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers. Biomed Opt Express 9(7):3049\u20133066","journal-title":"Biomed Opt Express"},{"issue":"11","key":"5826_CR14","doi-asserted-by":"publisher","first-page":"5759","DOI":"10.1364\/BOE.9.005759","volume":"9","author":"J Kugelman","year":"2018","unstructured":"Kugelman J, Alonso-Caneiro D, Read SA, Vincent SJ, Collins MJ (2018) Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search. Biomed Opt Express 9(11):5759\u20135777","journal-title":"Biomed Opt Express"},{"issue":"8","key":"5826_CR15","doi-asserted-by":"publisher","first-page":"3627","DOI":"10.1364\/BOE.8.003627","volume":"8","author":"AG Roy","year":"2017","unstructured":"Roy AG et al (2017) ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Express 8(8):3627\u20133642","journal-title":"Biomed Opt Express"},{"issue":"7","key":"5826_CR16","doi-asserted-by":"publisher","first-page":"3292","DOI":"10.1364\/BOE.8.003292","volume":"8","author":"FG Venhuizen","year":"2017","unstructured":"Venhuizen FG et al (2017) Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks. Biomed Opt Expres 8(7):3292\u20133316","journal-title":"Biomed Opt Expres"},{"key":"5826_CR17","doi-asserted-by":"crossref","unstructured":"Alonso-Caneiro D et al (2018) Automatic retinal and choroidal boundary segmentation in OCT images using patch-based supervised machine learning methods. In: Asian conference on computer vision (ACCV). Springer, Cham, pp 215\u2013228","DOI":"10.1007\/978-3-030-21074-8_17"},{"key":"5826_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-49816-4","volume":"9","author":"J Kugelman","year":"2019","unstructured":"Kugelman J et al (2019) Automatic choroidal segmentation in OCT images using supervised deep learning methods. Sci Rep 9:1\u201313","journal-title":"Sci Rep"},{"key":"5826_CR19","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: Annual conference on neural information processing systems (NIPS), pp 1106\u20131114"},{"key":"5826_CR20","doi-asserted-by":"crossref","unstructured":"Szegedy C et al. (2015) Going deeper with convolutions. In: Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"5826_CR21","unstructured":"Odena A, Olah C, Shlens J (2016) Conditional image synthesis with auxiliary classifier GANs. arXiv:1610.09585"},{"key":"5826_CR22","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T (2018) A style-based generator architecture for generative adversarial networks. arXiv:1812.04948","DOI":"10.1109\/CVPR.2019.00453"},{"key":"5826_CR23","doi-asserted-by":"crossref","unstructured":"Shin H et al (2018) Medical image synthesis for data augmentation and anonymization using generative adversarial networks. arXiv:1807.10225","DOI":"10.1007\/978-3-030-00536-8_1"},{"key":"5826_CR24","doi-asserted-by":"crossref","unstructured":"Bailo O, Ham D, Shin YM (2019) Red blood cell image generation for data augmentation using conditional generative adversarial networks. arXiv:1901.06219","DOI":"10.1109\/CVPRW.2019.00136"},{"key":"5826_CR25","doi-asserted-by":"crossref","unstructured":"Frid-Adar M et al (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. arXiv:1803.01229","DOI":"10.1016\/j.neucom.2018.09.013"},{"key":"5826_CR26","unstructured":"Goodfellow IJ et al (2014) Generative adversarial networks. arXiv: 1406.2661"},{"key":"5826_CR27","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv:1411.1784"},{"key":"5826_CR28","unstructured":"Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434"},{"key":"5826_CR29","doi-asserted-by":"crossref","unstructured":"Schlegl T, Seeb\u00f6ck P, Waldstein SM, Schmidt-Erfurth U, Langs G (2017) Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International conference on information processing in medical imaging. Springer, Cham, pp 146\u2013157","DOI":"10.1007\/978-3-319-59050-9_12"},{"issue":"12","key":"5826_CR30","doi-asserted-by":"publisher","first-page":"6205","DOI":"10.1364\/BOE.9.006205","volume":"9","author":"KJ Halupka","year":"2018","unstructured":"Halupka KJ et al (2018) Retinal optical coherence tomography image enhancement via deep learning. Biomed Opt Express 9(12):6205\u20136221","journal-title":"Biomed Opt Express"},{"issue":"9","key":"5826_CR31","doi-asserted-by":"publisher","first-page":"12289","DOI":"10.1364\/OE.27.012289","volume":"27","author":"Y Huang","year":"2019","unstructured":"Huang Y et al (2019) Simultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network. Opt Express 27(9):12289\u201312307","journal-title":"Opt Express"},{"issue":"1","key":"5826_CR32","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1364\/BOE.379978","volume":"11","author":"D Romo-Bucheli","year":"2020","unstructured":"Romo-Bucheli D et al (2020) Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina. Biomed Opt Express 11(1):346\u2013363","journal-title":"Biomed Opt Express"},{"key":"5826_CR33","unstructured":"Salimans T et al (2016) Improved techniques for training GANs. arXiv:1606.03498"},{"key":"5826_CR34","unstructured":"Arjovksy M, Chintala S, Bottou L (2016) Wasserstein GAN. arXiv:1701.07875"},{"key":"5826_CR35","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A (2017) Improved training of Wasserstein GANs. arXiv:1704.00028"},{"key":"5826_CR36","doi-asserted-by":"crossref","unstructured":"Kugelman J et al. (2019) Constructing synthetic chorio-retinal patches using generative adversarial networks. In: 2019 digital image computing: techniques and applications (DICTA). IEEE, pp 1\u20138","DOI":"10.1109\/DICTA47822.2019.8946089"},{"issue":"5","key":"5826_CR37","doi-asserted-by":"publisher","first-page":"3586","DOI":"10.1167\/iovs.13-11732","volume":"54","author":"SA Read","year":"2013","unstructured":"Read SA, Collins MJ, Vincent SJ, Alonso-Caneiro D (2013) Choroidal thickness in childhood. Invest Ophthamol Vis Sci 54(5):3586\u20133593","journal-title":"Invest Ophthamol Vis Sci"},{"key":"5826_CR38","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1097\/IAE.0000000000000464","volume":"35","author":"SA Read","year":"2015","unstructured":"Read SA, Collins MJ, Vincent SJ, Alonso-Caneiro D (2015) Macular retinal layer thickness in childhood. Retina 35:1223\u20131233","journal-title":"Retina"},{"issue":"1","key":"5826_CR39","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/BF01386390","volume":"1","author":"EW Dijkstra","year":"1959","unstructured":"Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1(1):269\u2013271","journal-title":"Numer Math"},{"key":"5826_CR40","unstructured":"Keras implementations of generative adversarial networks. [Online]. https:\/\/github.com\/eriklindernoren\/Keras-GAN\/. Accessed 03 Jul 2019"},{"key":"5826_CR41","unstructured":"High quality image generation with a modified version of auxiliary classifier GAN. https:\/\/github.com\/andrearama\/Deep-Auxiliary-Classifier-GAN. Accessed 03 Jul 2019"},{"key":"5826_CR42","unstructured":"Gal Y, Ghahramani Z (2015) A theoretically grounded application of dropout in recurrent neural networks. arXiv:1512.05287"},{"key":"5826_CR43","unstructured":"Minibatch Discrimination layer. https:\/\/github.com\/keras-team\/keras\/pull\/3677\/. Accessed 03 July 2019"},{"key":"5826_CR44","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"5826_CR45","doi-asserted-by":"crossref","unstructured":"Shrivastava A et al (2016) Learning from simulated and unsupervised images through adversarial training. arXiv:1612.07828","DOI":"10.1109\/CVPR.2017.241"},{"key":"5826_CR46","unstructured":"Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) GANS trained by a two time-scale update rule converge to a local nash equilibrium. arXiv:1706.08500"},{"key":"5826_CR47","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.media.2018.07.001","volume":"49","author":"H Zhao","year":"2018","unstructured":"Zhao H, Li H, Maurer-Stroh S, Cheng L (2018) Synthetisizing retinal and neuronal images with generative adversarial nets. Med Image Anal 49:14\u201326","journal-title":"Med Image Anal"},{"key":"5826_CR48","doi-asserted-by":"crossref","unstructured":"Mao X et al. (2016) Least squares generative adversarial networks. arXiv:1611.04076","DOI":"10.1109\/ICCV.2017.304"},{"key":"5826_CR49","unstructured":"Odean A, Olah C, Shlens J (2016) Conditional image synthesis with auxiliary classifier GANs. arXiv:1610.09585"},{"key":"5826_CR50","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu J, Zhou T, Efros AA (2016) Image-to-image translation with conditional adversarial networks. arXiv:1611.07004","DOI":"10.1109\/CVPR.2017.632"},{"key":"5826_CR51","unstructured":"Zhu J et al. (2017) Toward multimodal image-to-image translation. arXiv:1711.11586"},{"key":"5826_CR52","unstructured":"Karras T, Aila T, Laine S, Lehtinen J (2017) Progressive growing of GANs for improved quality, stability, and variation. arXiv:1710.10196"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05826-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05826-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05826-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T06:25:39Z","timestamp":1624515939000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-05826-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,10]]},"references-count":52,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["5826"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-05826-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,10]]},"assertion":[{"value":"21 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}