Deep generative models for automated muscle segmentation in computed tomography scanning.

Accurate gluteus medius (GMd) volume evaluation may aid in the analysis of muscular atrophy states and help gain an improved understanding of patient recovery via rehabilitation. However, the segmentation of muscle regions in GMd images for cubic muscle volume assessment is time-consuming and labor-...

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Autores principales:	Daisuke Nishiyama, Hiroshi Iwasaki, Takaya Taniguchi, Daisuke Fukui, Manabu Yamanaka, Teiji Harada, Hiroshi Yamada
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7dffd162dfd74a4294d4be45c3e2e508
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spelling	oai:doaj.org-article:7dffd162dfd74a4294d4be45c3e2e5082021-12-02T20:06:18ZDeep generative models for automated muscle segmentation in computed tomography scanning.1932-620310.1371/journal.pone.0257371https://doaj.org/article/7dffd162dfd74a4294d4be45c3e2e5082021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257371https://doaj.org/toc/1932-6203Accurate gluteus medius (GMd) volume evaluation may aid in the analysis of muscular atrophy states and help gain an improved understanding of patient recovery via rehabilitation. However, the segmentation of muscle regions in GMd images for cubic muscle volume assessment is time-consuming and labor-intensive. This study automated GMd-region segmentation from the computed tomography (CT) images of patients diagnosed with hip osteoarthritis using deep learning and evaluated the segmentation accuracy. To this end, 5250 augmented pairs of training data were obtained from five participants, and a conditional generative adversarial network was used to identify the relationships between the image pairs. Using the preserved test datasets, the results of automatic segmentation with the trained deep learning model were compared to those of manual segmentation in terms of the dice similarity coefficient (DSC), volume similarity (VS), and shape similarity (MS). As observed, the average DSC values for automatic and manual segmentations were 0.748 and 0.812, respectively, with a significant difference (p < 0.0001); the average VS values were 0.247 and 0.203, respectively, with no significant difference (p = 0.069); and the average MS values were 1.394 and 1.156, respectively, with no significant difference (p = 0.308). The GMd volumes obtained by automatic and manual segmentation were 246.2 cm3 and 282.9 cm3, respectively. The noninferiority of the DSC obtained by automatic segmentation was verified against that obtained by manual segmentation. Accordingly, the proposed GAN-based automatic GMd-segmentation technique is confirmed to be noninferior to manual segmentation. Therefore, the findings of this research confirm that the proposed method not only reduces time and effort but also facilitates accurate assessment of the cubic muscle volume.Daisuke NishiyamaHiroshi IwasakiTakaya TaniguchiDaisuke FukuiManabu YamanakaTeiji HaradaHiroshi YamadaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257371 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daisuke Nishiyama Hiroshi Iwasaki Takaya Taniguchi Daisuke Fukui Manabu Yamanaka Teiji Harada Hiroshi Yamada Deep generative models for automated muscle segmentation in computed tomography scanning.
description	Accurate gluteus medius (GMd) volume evaluation may aid in the analysis of muscular atrophy states and help gain an improved understanding of patient recovery via rehabilitation. However, the segmentation of muscle regions in GMd images for cubic muscle volume assessment is time-consuming and labor-intensive. This study automated GMd-region segmentation from the computed tomography (CT) images of patients diagnosed with hip osteoarthritis using deep learning and evaluated the segmentation accuracy. To this end, 5250 augmented pairs of training data were obtained from five participants, and a conditional generative adversarial network was used to identify the relationships between the image pairs. Using the preserved test datasets, the results of automatic segmentation with the trained deep learning model were compared to those of manual segmentation in terms of the dice similarity coefficient (DSC), volume similarity (VS), and shape similarity (MS). As observed, the average DSC values for automatic and manual segmentations were 0.748 and 0.812, respectively, with a significant difference (p < 0.0001); the average VS values were 0.247 and 0.203, respectively, with no significant difference (p = 0.069); and the average MS values were 1.394 and 1.156, respectively, with no significant difference (p = 0.308). The GMd volumes obtained by automatic and manual segmentation were 246.2 cm3 and 282.9 cm3, respectively. The noninferiority of the DSC obtained by automatic segmentation was verified against that obtained by manual segmentation. Accordingly, the proposed GAN-based automatic GMd-segmentation technique is confirmed to be noninferior to manual segmentation. Therefore, the findings of this research confirm that the proposed method not only reduces time and effort but also facilitates accurate assessment of the cubic muscle volume.
format	article
author	Daisuke Nishiyama Hiroshi Iwasaki Takaya Taniguchi Daisuke Fukui Manabu Yamanaka Teiji Harada Hiroshi Yamada
author_facet	Daisuke Nishiyama Hiroshi Iwasaki Takaya Taniguchi Daisuke Fukui Manabu Yamanaka Teiji Harada Hiroshi Yamada
author_sort	Daisuke Nishiyama
title	Deep generative models for automated muscle segmentation in computed tomography scanning.
title_short	Deep generative models for automated muscle segmentation in computed tomography scanning.
title_full	Deep generative models for automated muscle segmentation in computed tomography scanning.
title_fullStr	Deep generative models for automated muscle segmentation in computed tomography scanning.
title_full_unstemmed	Deep generative models for automated muscle segmentation in computed tomography scanning.
title_sort	deep generative models for automated muscle segmentation in computed tomography scanning.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7dffd162dfd74a4294d4be45c3e2e508
work_keys_str_mv	AT daisukenishiyama deepgenerativemodelsforautomatedmusclesegmentationincomputedtomographyscanning AT hiroshiiwasaki deepgenerativemodelsforautomatedmusclesegmentationincomputedtomographyscanning AT takayataniguchi deepgenerativemodelsforautomatedmusclesegmentationincomputedtomographyscanning AT daisukefukui deepgenerativemodelsforautomatedmusclesegmentationincomputedtomographyscanning AT manabuyamanaka deepgenerativemodelsforautomatedmusclesegmentationincomputedtomographyscanning AT teijiharada deepgenerativemodelsforautomatedmusclesegmentationincomputedtomographyscanning AT hiroshiyamada deepgenerativemodelsforautomatedmusclesegmentationincomputedtomographyscanning
_version_	1718375367776927744

Deep generative models for automated muscle segmentation in computed tomography scanning.

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