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Paper Abstract and Keywords
Presentation 2016-01-20 09:56
Automated liver segmentation from 3D MRI without parameter tuning for imaging condition
Yuto Masaki, Shunta Hirayama, Futoshi Yokota, Yoshito Otake (NAIST), Masatoshi Hori (Osaka Univ.), Toshiyuki Okada (Tsukuba Univ.), Noriyuki Tomiyama (Osaka Univ.), Yoshinobu Sato (NAIST) MI2015-114
Abstract (in Japanese) (See Japanese page) 
(in English) The automated segmentation of liver from MRI is useful for computer-aided diagnosis system to liver fibrosis.
Several previous studies reported automated liver segmentation method from MRI, however their evaluation conducts to only a
specific imaging condition MRI. The automated liver segmentation method that does not depend on imaging modality and
condition is preferable. In our previous study, we reported automated liver segmentation method from CT using estimation of
target specific intensity model by shape-location priors. The purpose of this study is to evaluate liver segmentation accuracy
from MRI applied our method. We conducted experiments that our method was applied to contrast enhanced MRI and noncontrast
enhanced gradient echo MRI with parameters and training dataset obtained by CT. As a result, the dice coefficients were
0.955±0.020 and 0.934±0.025, respectively. Experimental results showed that our liver segmentation method in MRI was useful.
Keyword (in Japanese) (See Japanese page) 
(in English) subject-specific priors / bias field correction / statistical shape model / probabilistic atlas / joint histogram / computer-aided diagnosis / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 401, MI2015-114, pp. 199-204, Jan. 2016.
Paper # MI2015-114 
Date of Issue 2016-01-12 (MI) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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Conference Information
Committee MI  
Conference Date 2016-01-19 - 2016-01-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Bunka Tenbusu Kan 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General topics in medical imaging 
Paper Information
Registration To MI 
Conference Code 2016-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automated liver segmentation from 3D MRI without parameter tuning for imaging condition 
Sub Title (in English)  
Keyword(1) subject-specific priors  
Keyword(2) bias field correction  
Keyword(3) statistical shape model  
Keyword(4) probabilistic atlas  
Keyword(5) joint histogram  
Keyword(6) computer-aided diagnosis  
Keyword(7)  
Keyword(8)  
1st Author's Name Yuto Masaki  
1st Author's Affiliation Graduate School of Information Science , Nara Institute of Science and Technology (NAIST)
2nd Author's Name Shunta Hirayama  
2nd Author's Affiliation Graduate School of Information Science , Nara Institute of Science and Technology (NAIST)
3rd Author's Name Futoshi Yokota  
3rd Author's Affiliation Graduate School of Information Science , Nara Institute of Science and Technology (NAIST)
4th Author's Name Yoshito Otake  
4th Author's Affiliation Graduate School of Information Science , Nara Institute of Science and Technology (NAIST)
5th Author's Name Masatoshi Hori  
5th Author's Affiliation Osaka University (Osaka Univ.)
6th Author's Name Toshiyuki Okada  
6th Author's Affiliation University of Tsukuba (Tsukuba Univ.)
7th Author's Name Noriyuki Tomiyama  
7th Author's Affiliation Osaka University (Osaka Univ.)
8th Author's Name Yoshinobu Sato  
8th Author's Affiliation Graduate School of Information Science , Nara Institute of Science and Technology (NAIST)
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Speaker Author-1 
Date Time 2016-01-20 09:56:00 
Presentation Time 12 minutes 
Registration for MI 
Paper # MI2015-114 
Volume (vol) vol.115 
Number (no) no.401 
Page pp.199-204 
#Pages
Date of Issue 2016-01-12 (MI) 


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