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Paper Abstract and Keywords
Presentation 2021-03-17 10:45
[Short Paper] Preliminary study for improving the performance of abdominal multi-phase CT image registration based on 3D deep CNN with a CycleGAN
Ryotaro Fuwa, Xiangong Zhou, Takeshi Hara, Hiroshi Fujita (Gifu Univ.) MI2020-90
Abstract (in Japanese) (See Japanese page) 
(in English) Deep learning is expected to be an approach to solve the problem of accurate medical image alignment. Recently, VoxelMorph, an unsupervised deep learning method, has attracted much attention as a 3D medical image alignment method. However, we did not obtain ideal results when using VoxelMorph to align contrast CT images to non-contrast CT images. One of the reasons for this is the effect of the variation of the shading distribution of the CT image due to the contrast effect. In order to solve this problem, we propose an approach to align 3D CT images after reducing the contrast effect of CT images by CycleGAN. The experimental results show that the proposed method is more effective than VoxelMorph in reducing noise and recognizing contrast effects in the liver region.
Keyword (in Japanese) (See Japanese page) 
(in English) Abdominal CT image / 3D image registration / CycleGAN / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 431, MI2020-90, pp. 182-185, March 2021.
Paper # MI2020-90 
Date of Issue 2021-03-08 (MI) 
ISSN 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 MI2020-90

Conference Information
Committee MI  
Conference Date 2021-03-15 - 2021-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging 
Paper Information
Registration To MI 
Conference Code 2021-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Preliminary study for improving the performance of abdominal multi-phase CT image registration based on 3D deep CNN with a CycleGAN 
Sub Title (in English)  
Keyword(1) Abdominal CT image  
Keyword(2) 3D image registration  
Keyword(3) CycleGAN  
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1st Author's Name Ryotaro Fuwa  
1st Author's Affiliation Gifu University (Gifu Univ.)
2nd Author's Name Xiangong Zhou  
2nd Author's Affiliation Gifu University (Gifu Univ.)
3rd Author's Name Takeshi Hara  
3rd Author's Affiliation Gifu University (Gifu Univ.)
4th Author's Name Hiroshi Fujita  
4th Author's Affiliation Gifu University (Gifu Univ.)
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Speaker Author-1 
Date Time 2021-03-17 10:45:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # MI2020-90 
Volume (vol) vol.120 
Number (no) no.431 
Page pp.182-185 
#Pages 4 
Date of Issue 2021-03-08 (MI) 

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