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Paper Abstract and Keywords
Presentation 2022-01-26 14:05
[Short Paper] Joint Learning for Multi-Phase CT Image Registration and Automatic Recognition of Anatomical Structures Based on a Deep Neural Network
Ryotaro Fuwa, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita (Gifu Univ.) MI2021-64
Abstract (in Japanese) (See Japanese page) 
(in English) Computer-aided diagnosis (CAD) systems require image registration and automatic recognition of anatomical structures on multiphase CT images. Deep learning has been expected to be an effective approach to realize those tasks. However, the recent works showed that the unsupervised deep learning could not provide accurate results and supervised deep learning required ground truth as supervisory signals which is difficult to obtain (especially for the vascular region) on non-contrast CT images. In order to address these issues, we propose a method to jointly learn the registration between multiphase CT images and segmentation process of multiple organs. In this paper, we applied the proposed method to 100 cases of multiphase CT images of human abdomen and evaluated the performance of each task by using Dice value based on human annotations. The preliminary results showed that the registration performance was improved by 1.3% and the organ segmentation performance was improved by 13.0% on Dice value comparing to the models trained for each task independently. These results indicated that the proposed method based on joint learning of different tasks was effective to accomplish the fundamental functions of CAD systems for in CT images.
Keyword (in Japanese) (See Japanese page) 
(in English) Multi-phase abdomen CT image / 3D Image Registration / 3D Image Segmentation / Joint Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 347, MI2021-64, pp. 82-85, Jan. 2022.
Paper # MI2021-64 
Date of Issue 2022-01-18 (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 MI2021-64

Conference Information
Committee MI  
Conference Date 2022-01-25 - 2022-01-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2022-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Joint Learning for Multi-Phase CT Image Registration and Automatic Recognition of Anatomical Structures Based on a Deep Neural Network 
Sub Title (in English)  
Keyword(1) Multi-phase abdomen CT image  
Keyword(2) 3D Image Registration  
Keyword(3) 3D Image Segmentation  
Keyword(4) Joint Learning  
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1st Author's Name Ryotaro Fuwa  
1st Author's Affiliation Gifu University (Gifu Univ.)
2nd Author's Name Xiangrong 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 2022-01-26 14:05:00 
Presentation Time 13 minutes 
Registration for MI 
Paper # MI2021-64 
Volume (vol) vol.121 
Number (no) no.347 
Page pp.82-85 
#Pages 4 
Date of Issue 2022-01-18 (MI) 

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