Coronary artery segmentation under class imbalance using a U-Net based architecture on computed tomography angiography images

Abstract Coronary artery disease is caused primarily by vessel narrowing. Extraction of the coronary artery area from images is the preferred procedure for diagnosing coronary diseases. In this study, a U-Net-based network architecture, 3D Dense-U-Net, was adopted to perform fully automatic segmenta...

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Autores principales:	Li-Syuan Pan, Chia-Wei Li, Shun-Feng Su, Shee-Yen Tay, Quoc-Viet Tran, Wing P. Chan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/70b534d4126a415188dd85fb35fd5adf
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spelling	oai:doaj.org-article:70b534d4126a415188dd85fb35fd5adf2021-12-02T16:08:05ZCoronary artery segmentation under class imbalance using a U-Net based architecture on computed tomography angiography images10.1038/s41598-021-93889-z2045-2322https://doaj.org/article/70b534d4126a415188dd85fb35fd5adf2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93889-zhttps://doaj.org/toc/2045-2322Abstract Coronary artery disease is caused primarily by vessel narrowing. Extraction of the coronary artery area from images is the preferred procedure for diagnosing coronary diseases. In this study, a U-Net-based network architecture, 3D Dense-U-Net, was adopted to perform fully automatic segmentation of the coronary artery. The network was applied to 474 coronary computed tomography (CT) angiography scans performed at Wanfang Hospital, Taiwan. Of these, 10% were used for testing. The CT scans were divided into patches of 16 original high-resolution slices. The slices were overlapped between patches to take advantage of surrounding imaging information. However, an imbalance between the foreground and background presents a challenge in smaller-object segmentation such as with coronary arteries. The network was optimized and achieved a promising result when the focal loss concept was adopted. To evaluate the accuracy of the automatic segmentation approach, the dice similarity coefficient (DSC) was calculated, and an existing clinical tool was used. The subjective ratings of three experienced radiologists were used to compare the two ratings. The results show that the proposed approach can achieve a DSC of 0.9691, which is significantly higher than other studies using a deep learning approach. In the main trunk, the results of automatic segmentation agree with those of the clinical tool; they were significantly better in some small branches. In our study, automatic segmentation tool shows high-performance detection in coronary lumen vessels, thereby providing potential power in assisting clinical diagnosis.Li-Syuan PanChia-Wei LiShun-Feng SuShee-Yen TayQuoc-Viet TranWing P. ChanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Li-Syuan Pan Chia-Wei Li Shun-Feng Su Shee-Yen Tay Quoc-Viet Tran Wing P. Chan Coronary artery segmentation under class imbalance using a U-Net based architecture on computed tomography angiography images
description	Abstract Coronary artery disease is caused primarily by vessel narrowing. Extraction of the coronary artery area from images is the preferred procedure for diagnosing coronary diseases. In this study, a U-Net-based network architecture, 3D Dense-U-Net, was adopted to perform fully automatic segmentation of the coronary artery. The network was applied to 474 coronary computed tomography (CT) angiography scans performed at Wanfang Hospital, Taiwan. Of these, 10% were used for testing. The CT scans were divided into patches of 16 original high-resolution slices. The slices were overlapped between patches to take advantage of surrounding imaging information. However, an imbalance between the foreground and background presents a challenge in smaller-object segmentation such as with coronary arteries. The network was optimized and achieved a promising result when the focal loss concept was adopted. To evaluate the accuracy of the automatic segmentation approach, the dice similarity coefficient (DSC) was calculated, and an existing clinical tool was used. The subjective ratings of three experienced radiologists were used to compare the two ratings. The results show that the proposed approach can achieve a DSC of 0.9691, which is significantly higher than other studies using a deep learning approach. In the main trunk, the results of automatic segmentation agree with those of the clinical tool; they were significantly better in some small branches. In our study, automatic segmentation tool shows high-performance detection in coronary lumen vessels, thereby providing potential power in assisting clinical diagnosis.
format	article
author	Li-Syuan Pan Chia-Wei Li Shun-Feng Su Shee-Yen Tay Quoc-Viet Tran Wing P. Chan
author_facet	Li-Syuan Pan Chia-Wei Li Shun-Feng Su Shee-Yen Tay Quoc-Viet Tran Wing P. Chan
author_sort	Li-Syuan Pan
title	Coronary artery segmentation under class imbalance using a U-Net based architecture on computed tomography angiography images
title_short	Coronary artery segmentation under class imbalance using a U-Net based architecture on computed tomography angiography images
title_full	Coronary artery segmentation under class imbalance using a U-Net based architecture on computed tomography angiography images
title_fullStr	Coronary artery segmentation under class imbalance using a U-Net based architecture on computed tomography angiography images
title_full_unstemmed	Coronary artery segmentation under class imbalance using a U-Net based architecture on computed tomography angiography images
title_sort	coronary artery segmentation under class imbalance using a u-net based architecture on computed tomography angiography images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/70b534d4126a415188dd85fb35fd5adf
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Coronary artery segmentation under class imbalance using a U-Net based architecture on computed tomography angiography images

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