Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography

Abstract Age-related macular degeneration (AMD) is a progressive retinal disease, causing vision loss. A more detailed characterization of its atrophic form became possible thanks to the introduction of Optical Coherence Tomography (OCT). However, manual atrophy quantification in 3D retinal scans is...

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Autores principales:	Yasmine Derradji, Agata Mosinska, Stefanos Apostolopoulos, Carlos Ciller, Sandro De Zanet, Irmela Mantel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/99cc5b247c774ecc9830aa9e194367de
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spelling	oai:doaj.org-article:99cc5b247c774ecc9830aa9e194367de2021-11-14T12:18:59ZFully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography10.1038/s41598-021-01227-02045-2322https://doaj.org/article/99cc5b247c774ecc9830aa9e194367de2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01227-0https://doaj.org/toc/2045-2322Abstract Age-related macular degeneration (AMD) is a progressive retinal disease, causing vision loss. A more detailed characterization of its atrophic form became possible thanks to the introduction of Optical Coherence Tomography (OCT). However, manual atrophy quantification in 3D retinal scans is a tedious task and prevents taking full advantage of the accurate retina depiction. In this study we developed a fully automated algorithm segmenting Retinal Pigment Epithelial and Outer Retinal Atrophy (RORA) in dry AMD on macular OCT. 62 SD-OCT scans from eyes with atrophic AMD (57 patients) were collected and split into train and test sets. The training set was used to develop a Convolutional Neural Network (CNN). The performance of the algorithm was established by cross validation and comparison to the test set with ground-truth annotated by two graders. Additionally, the effect of using retinal layer segmentation during training was investigated. The algorithm achieved mean Dice scores of 0.881 and 0.844, sensitivity of 0.850 and 0.915 and precision of 0.928 and 0.799 in comparison with Expert 1 and Expert 2, respectively. Using retinal layer segmentation improved the model performance. The proposed model identified RORA with performance matching human experts. It has a potential to rapidly identify atrophy with high consistency.Yasmine DerradjiAgata MosinskaStefanos ApostolopoulosCarlos CillerSandro De ZanetIrmela MantelNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yasmine Derradji Agata Mosinska Stefanos Apostolopoulos Carlos Ciller Sandro De Zanet Irmela Mantel Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography
description	Abstract Age-related macular degeneration (AMD) is a progressive retinal disease, causing vision loss. A more detailed characterization of its atrophic form became possible thanks to the introduction of Optical Coherence Tomography (OCT). However, manual atrophy quantification in 3D retinal scans is a tedious task and prevents taking full advantage of the accurate retina depiction. In this study we developed a fully automated algorithm segmenting Retinal Pigment Epithelial and Outer Retinal Atrophy (RORA) in dry AMD on macular OCT. 62 SD-OCT scans from eyes with atrophic AMD (57 patients) were collected and split into train and test sets. The training set was used to develop a Convolutional Neural Network (CNN). The performance of the algorithm was established by cross validation and comparison to the test set with ground-truth annotated by two graders. Additionally, the effect of using retinal layer segmentation during training was investigated. The algorithm achieved mean Dice scores of 0.881 and 0.844, sensitivity of 0.850 and 0.915 and precision of 0.928 and 0.799 in comparison with Expert 1 and Expert 2, respectively. Using retinal layer segmentation improved the model performance. The proposed model identified RORA with performance matching human experts. It has a potential to rapidly identify atrophy with high consistency.
format	article
author	Yasmine Derradji Agata Mosinska Stefanos Apostolopoulos Carlos Ciller Sandro De Zanet Irmela Mantel
author_facet	Yasmine Derradji Agata Mosinska Stefanos Apostolopoulos Carlos Ciller Sandro De Zanet Irmela Mantel
author_sort	Yasmine Derradji
title	Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography
title_short	Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography
title_full	Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography
title_fullStr	Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography
title_full_unstemmed	Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography
title_sort	fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/99cc5b247c774ecc9830aa9e194367de
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Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography

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