Automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method

Abstract Macular edema is considered as a major cause of visual loss and blindness in patients with ocular fundus diseases. Optical coherence tomography (OCT) is a non-invasive imaging technique, which has been widely applied for diagnosing macular edema due to its non-invasive and high resolution p...

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Autores principales:	Zhenhua Wang, Yuanfu Zhong, Mudi Yao, Yan Ma, Wenping Zhang, Chaopeng Li, Zhifu Tao, Qin Jiang, Biao Yan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f13d960fe6d44a629795d16f6cc1d629
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spelling	oai:doaj.org-article:f13d960fe6d44a629795d16f6cc1d6292021-12-02T18:18:32ZAutomated segmentation of macular edema for the diagnosis of ocular disease using deep learning method10.1038/s41598-021-92458-82045-2322https://doaj.org/article/f13d960fe6d44a629795d16f6cc1d6292021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92458-8https://doaj.org/toc/2045-2322Abstract Macular edema is considered as a major cause of visual loss and blindness in patients with ocular fundus diseases. Optical coherence tomography (OCT) is a non-invasive imaging technique, which has been widely applied for diagnosing macular edema due to its non-invasive and high resolution properties. However, the practical applications remain challenges due to the distorted retinal morphology and blurred boundaries near macular edema. Herein, we developed a novel deep learning model for the segmentation of macular edema in OCT images based on DeepLab framework (OCT-DeepLab). In this model, we used atrous spatial pyramid pooling (ASPP) to detect macular edema at multiple features and used the fully connected conditional random field (CRF) to refine the boundary of macular edema. OCT-DeepLab model was compared against the traditional hand-crafted methods (C-V and SBG) and the end-to-end methods (FCN, PSPnet, and U-net) to estimate the segmentation performance. OCT-DeepLab showed great advantage over the hand-crafted methods (C-V and SBG) and end-to-end methods (FCN, PSPnet, and U-net) as shown by higher precision, sensitivity, specificity, and F1-score. The segmentation performance of OCT-DeepLab was comparable to that of manual label, with an average area under the curve (AUC) of 0.963, which was superior to other end-to-end methods (FCN, PSPnet, and U-net). Collectively, OCT-DeepLab model is suitable for the segmentation of macular edema and assist ophthalmologists in the management of ocular disease.Zhenhua WangYuanfu ZhongMudi YaoYan MaWenping ZhangChaopeng LiZhifu TaoQin JiangBiao YanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhenhua Wang Yuanfu Zhong Mudi Yao Yan Ma Wenping Zhang Chaopeng Li Zhifu Tao Qin Jiang Biao Yan Automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method
description	Abstract Macular edema is considered as a major cause of visual loss and blindness in patients with ocular fundus diseases. Optical coherence tomography (OCT) is a non-invasive imaging technique, which has been widely applied for diagnosing macular edema due to its non-invasive and high resolution properties. However, the practical applications remain challenges due to the distorted retinal morphology and blurred boundaries near macular edema. Herein, we developed a novel deep learning model for the segmentation of macular edema in OCT images based on DeepLab framework (OCT-DeepLab). In this model, we used atrous spatial pyramid pooling (ASPP) to detect macular edema at multiple features and used the fully connected conditional random field (CRF) to refine the boundary of macular edema. OCT-DeepLab model was compared against the traditional hand-crafted methods (C-V and SBG) and the end-to-end methods (FCN, PSPnet, and U-net) to estimate the segmentation performance. OCT-DeepLab showed great advantage over the hand-crafted methods (C-V and SBG) and end-to-end methods (FCN, PSPnet, and U-net) as shown by higher precision, sensitivity, specificity, and F1-score. The segmentation performance of OCT-DeepLab was comparable to that of manual label, with an average area under the curve (AUC) of 0.963, which was superior to other end-to-end methods (FCN, PSPnet, and U-net). Collectively, OCT-DeepLab model is suitable for the segmentation of macular edema and assist ophthalmologists in the management of ocular disease.
format	article
author	Zhenhua Wang Yuanfu Zhong Mudi Yao Yan Ma Wenping Zhang Chaopeng Li Zhifu Tao Qin Jiang Biao Yan
author_facet	Zhenhua Wang Yuanfu Zhong Mudi Yao Yan Ma Wenping Zhang Chaopeng Li Zhifu Tao Qin Jiang Biao Yan
author_sort	Zhenhua Wang
title	Automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method
title_short	Automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method
title_full	Automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method
title_fullStr	Automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method
title_full_unstemmed	Automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method
title_sort	automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f13d960fe6d44a629795d16f6cc1d629
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Automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method

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