ChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in Optical Coherence Tomography Images

Understanding the changes in choroidal thickness and vasculature is important to monitor the development and progression of various ophthalmic diseases. Accurate segmentation of the choroid layer and choroidal vessels is critical to better analyze and understand the choroidal changes. In this study,...

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Autores principales:	Tin Tin Khaing, Takayuki Okamoto, Chen Ye, Md. Abdul Mannan, Hirotaka Yokouchi, Kazuya Nakano, Pakinee Aimmanee, Stanislav S. Makhanov, Hideaki Haneishi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Choroid layer choroidal vessels ChoroidNET dense dilated U-Net optical coherence tomography (OCT) Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/1ca55a3328a546119442156a17d73605
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spelling	oai:doaj.org-article:1ca55a3328a546119442156a17d736052021-11-18T00:08:55ZChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in Optical Coherence Tomography Images2169-353610.1109/ACCESS.2021.3124993https://doaj.org/article/1ca55a3328a546119442156a17d736052021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9599663/https://doaj.org/toc/2169-3536Understanding the changes in choroidal thickness and vasculature is important to monitor the development and progression of various ophthalmic diseases. Accurate segmentation of the choroid layer and choroidal vessels is critical to better analyze and understand the choroidal changes. In this study, we develop a dense dilated U-Net model (ChoroidNET) for segmenting the choroid layer and choroidal vessels in optical coherence tomography (OCT) images. The performance of ChoroidNET is evaluated using an OCT dataset that contains images with various retinal pathologies. Overall Dice coefficient of 95.1 ± 0.4 and 82.4 ± 2.4 were obtained for choroid layer and vessel segmentation, respectively. Comparisons show that among state-of-the-art models, ChoroidNET, which produces results that are consistent with ground truths, is the most robust segmentation framework.Tin Tin KhaingTakayuki OkamotoChen YeMd. Abdul MannanHirotaka YokouchiKazuya NakanoPakinee AimmaneeStanislav S. MakhanovHideaki HaneishiIEEEarticleChoroid layerchoroidal vesselsChoroidNETdense dilated U-Netoptical coherence tomography (OCT)Electrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150951-150965 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Choroid layer choroidal vessels ChoroidNET dense dilated U-Net optical coherence tomography (OCT) Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Choroid layer choroidal vessels ChoroidNET dense dilated U-Net optical coherence tomography (OCT) Electrical engineering. Electronics. Nuclear engineering TK1-9971 Tin Tin Khaing Takayuki Okamoto Chen Ye Md. Abdul Mannan Hirotaka Yokouchi Kazuya Nakano Pakinee Aimmanee Stanislav S. Makhanov Hideaki Haneishi ChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in Optical Coherence Tomography Images
description	Understanding the changes in choroidal thickness and vasculature is important to monitor the development and progression of various ophthalmic diseases. Accurate segmentation of the choroid layer and choroidal vessels is critical to better analyze and understand the choroidal changes. In this study, we develop a dense dilated U-Net model (ChoroidNET) for segmenting the choroid layer and choroidal vessels in optical coherence tomography (OCT) images. The performance of ChoroidNET is evaluated using an OCT dataset that contains images with various retinal pathologies. Overall Dice coefficient of 95.1 ± 0.4 and 82.4 ± 2.4 were obtained for choroid layer and vessel segmentation, respectively. Comparisons show that among state-of-the-art models, ChoroidNET, which produces results that are consistent with ground truths, is the most robust segmentation framework.
format	article
author	Tin Tin Khaing Takayuki Okamoto Chen Ye Md. Abdul Mannan Hirotaka Yokouchi Kazuya Nakano Pakinee Aimmanee Stanislav S. Makhanov Hideaki Haneishi
author_facet	Tin Tin Khaing Takayuki Okamoto Chen Ye Md. Abdul Mannan Hirotaka Yokouchi Kazuya Nakano Pakinee Aimmanee Stanislav S. Makhanov Hideaki Haneishi
author_sort	Tin Tin Khaing
title	ChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in Optical Coherence Tomography Images
title_short	ChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in Optical Coherence Tomography Images
title_full	ChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in Optical Coherence Tomography Images
title_fullStr	ChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in Optical Coherence Tomography Images
title_full_unstemmed	ChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in Optical Coherence Tomography Images
title_sort	choroidnet: a dense dilated u-net model for choroid layer and vessel segmentation in optical coherence tomography images
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/1ca55a3328a546119442156a17d73605
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ChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in Optical Coherence Tomography Images

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