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

Number of Access:702（2025-09-06 08:11 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/93078

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Title
Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas
Creator
NRID
1000030397773 1000030397773
ORCID
0000-0002-4661-8821 0000-0002-4661-8821
researchmap permalink
YamaguchiS_30397773 YamaguchiS_30397773
Scopus Author ID
7403873463 7403873463
CreatorName
Yamaguchi, Satoshi
CreatorName
Satake, Koji
CreatorName
Yamaji, Yoshio
CreatorName
Chen, Yen Wei
NRID
1000010268154 1000010268154
CreatorName
Tanaka, Hiromi T.
Subject
Computed tomography
Liver segmentation
Liver/AH
Probabilistic models
Region-growing methodology
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.
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.
Publisher
Elsevier Ltd
Source Title
Computers in Biology and Medicine
Volume (Issue)
55
Page
79-85
Date of Issued
2014-12-01
Language
English
Handle URL
https://hdl.handle.net/11094/93078
PISSN
00104825
Relation.isVersionOf
DOI (Publisher Version)
https://doi.org/10.1016/j.compbiomed.2014.10.003
Access Rights
open access
CopyRight
© 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/
oaire:version
Accepted Manuscript
Category
学術雑誌論文 Journal Article