Automatic Segmentation of Supraspinatus Muscle via Bone-Based Localization in Torso Computed Tomography Images Using U-Net

The supraspinatus tendon is the most frequently torn tendon in the rotator cuff. Rotator cuff reconstruction is more likely to result in retear if the muscle has atrophy or fatty degeneration. Thus, atrophy and fatty degeneration of the supraspinatus muscle are predictors of the postoperative course...

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Autores principales:	Yuichi Wakamatsu, Naoki Kamiya, Xiangrong Zhou, Hiroki Kato, Takeshi Hara, Hiroshi Fujita
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Computed tomography scapula segmentation supraspinatus muscle U-Net Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/407c1e3014de4dcbbcda793d43d4dd11
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id	oai:doaj.org-article:407c1e3014de4dcbbcda793d43d4dd11
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spelling	oai:doaj.org-article:407c1e3014de4dcbbcda793d43d4dd112021-11-26T00:01:10ZAutomatic Segmentation of Supraspinatus Muscle via Bone-Based Localization in Torso Computed Tomography Images Using U-Net2169-353610.1109/ACCESS.2021.3127565https://doaj.org/article/407c1e3014de4dcbbcda793d43d4dd112021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9612210/https://doaj.org/toc/2169-3536The supraspinatus tendon is the most frequently torn tendon in the rotator cuff. Rotator cuff reconstruction is more likely to result in retear if the muscle has atrophy or fatty degeneration. Thus, atrophy and fatty degeneration of the supraspinatus muscle are predictors of the postoperative course, and volume analysis using three-dimensional segmentation of the supraspinatus muscle is necessary. The supraspinatus muscle is attached to the scapula, making it possible to estimate the region of the muscle based on the position of the scapula. In this paper, we propose a supraspinatus muscle segmentation method based on the scapula position in torso computed tomography (CT) images. Our proposed method consists of supraspinatus muscle localization using a scapula segmentation result and supraspinatus muscle segmentation based on the localization result. U-Net is used for scapula and supraspinatus muscle segmentation. In this experiment, we used torso CT images and pseudo-chest CT images which were generated from the scans of the same patient. The mean Dice values of the segmentation results obtained by applying the proposed method to the torso and pseudo-chest CT images were both 0.881. When localization was not used, the mean Dice values of the segmentation results in the torso and pseudo-chest CT images were 0.000 and 0.850, respectively. The experimental results demonstrate the effectiveness of bone-based localization in supraspinatus muscle segmentation using U-Net.Yuichi WakamatsuNaoki KamiyaXiangrong ZhouHiroki KatoTakeshi HaraHiroshi FujitaIEEEarticleComputed tomographyscapulasegmentationsupraspinatus muscleU-NetElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155555-155563 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computed tomography scapula segmentation supraspinatus muscle U-Net Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Computed tomography scapula segmentation supraspinatus muscle U-Net Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yuichi Wakamatsu Naoki Kamiya Xiangrong Zhou Hiroki Kato Takeshi Hara Hiroshi Fujita Automatic Segmentation of Supraspinatus Muscle via Bone-Based Localization in Torso Computed Tomography Images Using U-Net
description	The supraspinatus tendon is the most frequently torn tendon in the rotator cuff. Rotator cuff reconstruction is more likely to result in retear if the muscle has atrophy or fatty degeneration. Thus, atrophy and fatty degeneration of the supraspinatus muscle are predictors of the postoperative course, and volume analysis using three-dimensional segmentation of the supraspinatus muscle is necessary. The supraspinatus muscle is attached to the scapula, making it possible to estimate the region of the muscle based on the position of the scapula. In this paper, we propose a supraspinatus muscle segmentation method based on the scapula position in torso computed tomography (CT) images. Our proposed method consists of supraspinatus muscle localization using a scapula segmentation result and supraspinatus muscle segmentation based on the localization result. U-Net is used for scapula and supraspinatus muscle segmentation. In this experiment, we used torso CT images and pseudo-chest CT images which were generated from the scans of the same patient. The mean Dice values of the segmentation results obtained by applying the proposed method to the torso and pseudo-chest CT images were both 0.881. When localization was not used, the mean Dice values of the segmentation results in the torso and pseudo-chest CT images were 0.000 and 0.850, respectively. The experimental results demonstrate the effectiveness of bone-based localization in supraspinatus muscle segmentation using U-Net.
format	article
author	Yuichi Wakamatsu Naoki Kamiya Xiangrong Zhou Hiroki Kato Takeshi Hara Hiroshi Fujita
author_facet	Yuichi Wakamatsu Naoki Kamiya Xiangrong Zhou Hiroki Kato Takeshi Hara Hiroshi Fujita
author_sort	Yuichi Wakamatsu
title	Automatic Segmentation of Supraspinatus Muscle via Bone-Based Localization in Torso Computed Tomography Images Using U-Net
title_short	Automatic Segmentation of Supraspinatus Muscle via Bone-Based Localization in Torso Computed Tomography Images Using U-Net
title_full	Automatic Segmentation of Supraspinatus Muscle via Bone-Based Localization in Torso Computed Tomography Images Using U-Net
title_fullStr	Automatic Segmentation of Supraspinatus Muscle via Bone-Based Localization in Torso Computed Tomography Images Using U-Net
title_full_unstemmed	Automatic Segmentation of Supraspinatus Muscle via Bone-Based Localization in Torso Computed Tomography Images Using U-Net
title_sort	automatic segmentation of supraspinatus muscle via bone-based localization in torso computed tomography images using u-net
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/407c1e3014de4dcbbcda793d43d4dd11
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_version_	1718409963214209024

Automatic Segmentation of Supraspinatus Muscle via Bone-Based Localization in Torso Computed Tomography Images Using U-Net

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