Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed.

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 th...

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Autores principales:	Jefferson Alves Sousa, Anselmo Paiva, Aristófanes Silva, João Dallyson Almeida, Geraldo Braz Junior, João Otávio Diniz, Weslley Kelson Figueredo, Marcelo Gattass
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0ddcaa562ed24cd8881a1e60d3ebd499
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spelling	oai:doaj.org-article:0ddcaa562ed24cd8881a1e60d3ebd4992021-12-02T20:04:02ZAutomatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed.1932-620310.1371/journal.pone.0251591https://doaj.org/article/0ddcaa562ed24cd8881a1e60d3ebd4992021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251591https://doaj.org/toc/1932-6203Age-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's 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's 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.Jefferson Alves SousaAnselmo PaivaAristófanes SilvaJoão Dallyson AlmeidaGeraldo Braz JuniorJoão Otávio DinizWeslley Kelson FigueredoMarcelo GattassPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251591 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jefferson Alves Sousa Anselmo Paiva Aristófanes Silva João Dallyson Almeida Geraldo Braz Junior João Otávio Diniz Weslley Kelson Figueredo Marcelo Gattass Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed.
description	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's 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's 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.
format	article
author	Jefferson Alves Sousa Anselmo Paiva Aristófanes Silva João Dallyson Almeida Geraldo Braz Junior João Otávio Diniz Weslley Kelson Figueredo Marcelo Gattass
author_facet	Jefferson Alves Sousa Anselmo Paiva Aristófanes Silva João Dallyson Almeida Geraldo Braz Junior João Otávio Diniz Weslley Kelson Figueredo Marcelo Gattass
author_sort	Jefferson Alves Sousa
title	Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed.
title_short	Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed.
title_full	Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed.
title_fullStr	Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed.
title_full_unstemmed	Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed.
title_sort	automatic segmentation of retinal layers in oct images with intermediate age-related macular degeneration using u-net and dexined.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/0ddcaa562ed24cd8881a1e60d3ebd499
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Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed.

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