{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T06:53:31Z","timestamp":1789628011173,"version":"build-2803163510"},"reference-count":25,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T00:00:00Z","timestamp":1620950400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["Finance Code 001"],"award-info":[{"award-number":["Finance Code 001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003758","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa e ao Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico do Maranh\u00e3o","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003758","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Age-related macular degeneration (AMD) is an eye disease that can cause visual impairment and affects the elderly over 50 years of age. AMD is characterized by the presence of drusen, which causes changes in the physiological structure of the retinal pigment epithelium (RPE) and the boundaries of the Bruch\u2019s membrane layer (BM). Optical coherence tomography is one of the main exams for the detection and monitoring of AMD, which seeks changes through the evaluation of successive sectional cuts in the search for morphological changes caused by drusen. The use of CAD (Computer-Aided Detection) systems has contributed to increasing the chances of correct detection, assisting specialists in diagnosing and monitoring disease. Thus, the objective of this work is to present a method for the segmentation of the inner limiting membrane (ILM), retinal pigment epithelium, and Bruch\u2019s membrane in OCT images of healthy and Intermediate AMD patients. The method uses two deep neural networks, U-Net and DexiNed to perform the segmentation. The results were promising, reaching an average absolute error of 0.49 pixel for ILM, 0.57 for RPE, and 0.66 for BM.<\/jats:p>","DOI":"10.1371\/journal.pone.0251591","type":"journal-article","created":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T13:33:49Z","timestamp":1620999229000},"page":"e0251591","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":37,"title":["Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5928-9959","authenticated-orcid":true,"given":"Jefferson Alves","family":"Sousa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anselmo","family":"Paiva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arist\u00f3fanes","family":"Silva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o Dallyson","family":"Almeida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3731-6431","authenticated-orcid":true,"given":"Geraldo","family":"Braz Junior","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3303-3346","authenticated-orcid":true,"given":"Jo\u00e3o Ot\u00e1vio","family":"Diniz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weslley Kelson","family":"Figueredo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelo","family":"Gattass","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,5,14]]},"reference":[{"key":"pone.0251591.ref001","unstructured":"WHO WHO. Blindness and vision impairment; 2019. Available from: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/blindness-and-visual-impairment."},{"issue":"1","key":"pone.0251591.ref002","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.amepre.2013.02.018","article-title":"Age-related eye diseases and visual impairment among US adults","volume":"45","author":"CF Chou","year":"2013","journal-title":"American journal of preventive medicine"},{"key":"pone.0251591.ref003","unstructured":"Rim PHH. Age-Related Macular Degeneration: Study of the Risk Factors in a Brazilian Population. 2012;."},{"issue":"2","key":"pone.0251591.ref004","doi-asserted-by":"crossref","first-page":"e106","DOI":"10.1016\/S2214-109X(13)70145-1","article-title":"Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis","volume":"2","author":"WL Wong","year":"2014","journal-title":"The Lancet Global Health"},{"issue":"5035","key":"pone.0251591.ref005","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1126\/science.1957169","article-title":"Optical coherence tomography","volume":"254","author":"D Huang","year":"1991","journal-title":"Science"},{"issue":"6","key":"pone.0251591.ref006","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1097\/00006982-200509000-00002","article-title":"Comparison of fluorescein angiography and optical coherence tomography for patients with choroidal neovascularization after photodynamic therapy","volume":"25","author":"N Eter","year":"2005","journal-title":"Retina"},{"issue":"1","key":"pone.0251591.ref007","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":"SJ Chiu","year":"2012","journal-title":"Investigative Ophthalmology & Visual Science"},{"key":"pone.0251591.ref008","first-page":"1","volume-title":"Proceedings\u2026. vol. 6844. SPIE BiOS\u2014Ophthalmic Technologies","author":"S Farsiu","year":"2008"},{"issue":"5","key":"pone.0251591.ref009","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":"L Fang","year":"2017","journal-title":"Biomedical Optics Express"},{"key":"pone.0251591.ref010","doi-asserted-by":"crossref","unstructured":"Gopinath K, Rangrej SB, Sivaswamy J. A deep learning framework for segmentation of retinal layers from OCT images. In: 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR). IEEE; 2017. p. 888\u2013893.","DOI":"10.1109\/ACPR.2017.121"},{"issue":"18","key":"pone.0251591.ref011","doi-asserted-by":"crossref","first-page":"19413","DOI":"10.1364\/OE.18.019413","article-title":"Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation","volume":"18","author":"SJ Chiu","year":"2010","journal-title":"Biomedical Optics Express"},{"issue":"8","key":"pone.0251591.ref012","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":"J Tian","year":"2015","journal-title":"PloS one"},{"key":"pone.0251591.ref013","doi-asserted-by":"crossref","unstructured":"Chakravarty A, Sivaswamy J. End-to-end learning of a conditional random field for intra-retinal layer segmentation in optical coherence tomography. In: Annual Conference on Medical Image Understanding and Analysis. Springer; 2017. p. 3\u201314.","DOI":"10.1007\/978-3-319-60964-5_1"},{"issue":"11","key":"pone.0251591.ref014","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":"J Kugelman","year":"2018","journal-title":"Biomedical Optics Express"},{"key":"pone.0251591.ref015","unstructured":"Santos AM. Diagnosis of Age-Related Macular Degeneration from Optical Coherence Tomography using Geostatistics and Capsule Network. 2019;."},{"issue":"1","key":"pone.0251591.ref016","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":"S Farsiu","year":"2014","journal-title":"Ophthalmology"},{"issue":"9","key":"pone.0251591.ref017","first-page":"1401","article-title":"Automated retinal layer segmentation in OCT images of age-related macular degeneration","volume":"13","author":"Z Chen","year":"2019","journal-title":"IET Image Processing"},{"key":"pone.0251591.ref018","doi-asserted-by":"crossref","first-page":"53678","DOI":"10.1109\/ACCESS.2020.2981206","article-title":"Fast Retinal Segmentation Based on the Wave Algorithm","volume":"8","author":"S Lou","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0251591.ref019","unstructured":"Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In: Sixth international conference on computer vision (IEEE Cat. No. 98CH36271). IEEE; 1998. p. 839\u2013846."},{"issue":"1","key":"pone.0251591.ref020","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"N Otsu","year":"1979","journal-title":"IEEE transactions on systems, man, and cybernetics"},{"key":"pone.0251591.ref021","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer; 2015. p. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"pone.0251591.ref022","doi-asserted-by":"crossref","unstructured":"Soria X, Riba E, Sappa A. Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection. In: The IEEE Winter Conference on Applications of Computer Vision (WACV\u201920); 2020.","DOI":"10.1109\/WACV45572.2020.9093290"},{"key":"pone.0251591.ref023","doi-asserted-by":"crossref","unstructured":"Xie S, Tu Z. Holistically-nested edge detection. In: Proceedings of the IEEE international conference on computer vision; 2015. p. 1395\u20131403.","DOI":"10.1109\/ICCV.2015.164"},{"key":"pone.0251591.ref024","unstructured":"Chollet F, et al. Keras: The python deep learning library. Astrophysics Source Code Library. 2018;."},{"key":"pone.0251591.ref025","article-title":"OpenCV","volume":"3","author":"G Bradski","year":"2000","journal-title":"Dr Dobb\u2019s journal of software tools"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0251591","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T13:34:16Z","timestamp":1620999256000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0251591"}},"subtitle":[],"editor":[{"given":"Alfred S","family":"Lewin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,5,14]]},"references-count":25,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,5,14]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0251591","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,14]]}}}