Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas

Yamaguchi, Satoshi; Satake, Koji; Yamaji, Yoshio; Chen, Yen Wei; Tanaka, Hiromi T.

doi:https://doi.org/10.1016/j.compbiomed.2014.10.003

Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas

Yamaguchi, Satoshi; Satake, Koji; Yamaji, Yoshio et al.

Computers in Biology and Medicine, 2014, 55, 79-85

アクセス数:700件（2025-09-04 13:25 集計）

固定URL: https://hdl.handle.net/11094/93078

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タイトル	Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas
著者	NRID 1000030397773 1000030397773 ORCID 0000-0002-4661-8821 0000-0002-4661-8821 researchmap permalink YamaguchiS_30397773 YamaguchiS_30397773 Scopus Author ID 7403873463 7403873463 著者名 Yamaguchi, Satoshi
	著者名 Satake, Koji
	著者名 Yamaji, Yoshio
	著者名 Chen, Yen Wei
	NRID 1000010268154 1000010268154 著者名 Tanaka, Hiromi T.
キーワード等	Computed tomography
	Liver segmentation
	Liver/AH
	Probabilistic models
	Region-growing methodology
内容	Yamaguchi S., Satake K., Yamaji Y., et al. Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas. Computers in Biology and Medicine 55, 79 (2014); https://doi.org/10.1016/j.compbiomed.2014.10.003.
抄録	Background: We sought to evaluate a new regional segmentation method for use with three-dimensional (3D) non-contrast abdominal CT images and to report the preliminary results. Methods: The proposed method was evaluated in ten cases. Manually segmented areas were used as the gold standard for evaluation. To compare the standard and the extracted liver regions, the degree of coincidence R% was redefined by transforming a volumetric overlap error. We also evaluated the influence of varying the density window size in terms of setting the starting points. Results: We confirmed in ten cases that our method could segment the liver region more precisely than the conventional method. A size of window 15 voxels was optimal as the starting point in all cases. Conclusions: We demonstrated the accuracy of a 3D semiautomatic liver segmentation method for non-contrast CT. This method promises to offer radiologists a time-efficient segmentation aid.
出版者	Elsevier Ltd
収録物名	Computers in Biology and Medicine
巻(号)	55
ページ	79-85
刊行年月	2014-12-01
言語	英語
ハンドルURL	https://hdl.handle.net/11094/93078
PISSN	00104825
関連情報 (isVersionOf)	DOI (出版社版) https://doi.org/10.1016/j.compbiomed.2014.10.003
アクセス権	open access
権利情報	© 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
出版タイプ	Accepted Manuscript
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Computers in Biology and Medicine. 2014, 55, p. 79-85
DC.title	Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas
DC.creator	Yamaguchi, Satoshi
	Satake, Koji
	Yamaji, Yoshio
	Chen, Yen Wei
	Tanaka, Hiromi T.
DC.publisher	Elsevier Ltd
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2014-12-01
DC.identifier	https://doi.org/10.1016/j.compbiomed.2014.10.003
DC.subject	Computed tomography
	Liver segmentation
	Liver/AH
	Probabilistic models
	Region-growing methodology
DC.description	Yamaguchi S., Satake K., Yamaji Y., et al. Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas. Computers in Biology and Medicine 55, 79 (2014); https://doi.org/10.1016/j.compbiomed.2014.10.003.
DCTERMS.abstract	Background: We sought to evaluate a new regional segmentation method for use with three-dimensional (3D) non-contrast abdominal CT images and to report the preliminary results. Methods: The proposed method was evaluated in ten cases. Manually segmented areas were used as the gold standard for evaluation. To compare the standard and the extracted liver regions, the degree of coincidence R% was redefined by transforming a volumetric overlap error. We also evaluated the influence of varying the density window size in terms of setting the starting points. Results: We confirmed in ten cases that our method could segment the liver region more precisely than the conventional method. A size of window 15 voxels was optimal as the starting point in all cases. Conclusions: We demonstrated the accuracy of a 3D semiautomatic liver segmentation method for non-contrast CT. This method promises to offer radiologists a time-efficient segmentation aid.
DC.rights	© 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
DC.format	93078
citation_title	Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas
citation_author	Yamaguchi, Satoshi
	Satake, Koji
	Yamaji, Yoshio
	Chen, Yen Wei
	Tanaka, Hiromi T.
citation_publisher	Elsevier Ltd
citation_language	英語
citation_date	2014-12-01
citation_journal_title	Computers in Biology and Medicine
citation_volume	55
citation_firstpage	79
citation_lastpage	85
citation_doi	https://doi.org/10.1016/j.compbiomed.2014.10.003
citation_public_url	https://hdl.handle.net/11094/93078

Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas

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