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Paper Abstract and Keywords
Presentation 2019-01-23 14:00
Study on Automated Labeling of Abdominal Arteries Using Machine Learning with Data Augmentation
Yusuke Tetsumura, Yuichiro Hayashi, Masahiro Oda (Nagoya Univ), Takayuki Kitasaka (AIT), Kazunari Misawa (ACC), Kensaku Mori (Nagoya Univ) MI2018-106
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we improve automated anatomical labeling accuracy for the abdominal arteries by introducing data augmentation technique. It is important to understand the blood vessel structure of patient accurately before or during surgery. Several blood vessel labeling methods using machine learning have been proposed. However, the automated labeling accuracy was low for cases having minor branching patterns in the hepatic arteries since the number of such cases is not enough for learning. In this paper, we artificially expanded number of cases having minor branching patterns and performed balancing of sample number. In our experiments, automated labeling accuracy for the hepatic arteries of cases having minor branching patterns was 85.7% on average while the previous method achieved 82.2%.
Keyword (in Japanese) (See Japanese page) 
(in English) blood vessel / CT volume / anatomical names recognition / blood vessel structures analysis / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 412, MI2018-106, pp. 191-196, Jan. 2019.
Paper # MI2018-106 
Date of Issue 2019-01-15 (MI) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
and
reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF MI2018-106

Conference Information
Committee MI  
Conference Date 2019-01-22 - 2019-01-23 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2019-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on Automated Labeling of Abdominal Arteries Using Machine Learning with Data Augmentation 
Sub Title (in English)  
Keyword(1) blood vessel  
Keyword(2) CT volume  
Keyword(3) anatomical names recognition  
Keyword(4) blood vessel structures analysis  
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1st Author's Name Yusuke Tetsumura  
1st Author's Affiliation Nagoya University (Nagoya Univ)
2nd Author's Name Yuichiro Hayashi  
2nd Author's Affiliation Nagoya University (Nagoya Univ)
3rd Author's Name Masahiro Oda  
3rd Author's Affiliation Nagoya University (Nagoya Univ)
4th Author's Name Takayuki Kitasaka  
4th Author's Affiliation Aichi Institute of Technology (AIT)
5th Author's Name Kazunari Misawa  
5th Author's Affiliation Aichi Cancer Center Hospital (ACC)
6th Author's Name Kensaku Mori  
6th Author's Affiliation Nagoya University (Nagoya Univ)
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Speaker
Date Time 2019-01-23 14:00:00 
Presentation Time 60 
Registration for MI 
Paper # IEICE-MI2018-106 
Volume (vol) IEICE-118 
Number (no) no.412 
Page pp.191-196 
#Pages IEICE-6 
Date of Issue IEICE-MI-2019-01-15 


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