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Paper Abstract and Keywords
Presentation 2023-03-06 13:15
[Short Paper] Improvement of Small Organ Accuracy in Multi-Organ Segmentation of Abdominal CT Images Using 2.5D Deformable Convolutional CNN
Yuya Okumura, Hiroyuki Kudo, Hotaka Takizawa (Univ of Tsukuba) MI2022-80
Abstract (in Japanese) (See Japanese page) 
(in English) In multi-organ segmentation of abdominal CT images using deep learning, small organs such as the pancreas are difficult to recognize because they are affected by other regions due to their small size in addition to their large anatomical variability. Therefore, in this study, we improve the accuracy of small organs by extracting only the pancreas after performing multi-organ segmentation, training it again, and integrating it into the original results.Furthermore, we propose a 2.5D method that learns in three 2D cross sections (xy-horizontal, yz-horizontal, and xz-horizontal) using a deformable convolutional CNN that can absorb individual differences and misalignment of organ structures using displacement vector fields, and integrates the results by majority voting to obtain 3D results. Experimental results on an abdominal CT image dataset show that the proposed method is more accurate than conventional deep learning methods due to the extraction and re-training of small organs and the introduction of deformable convolution, and that the computational complexity is realistic for a 2.5D method.
Keyword (in Japanese) (See Japanese page) 
(in English) CT images / Deep learning / Convolutional Neural Networks / 3D CT images / Computer-aided Detection Systems / Automatic recognition and detection of anatomical structures / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 417, MI2022-80, pp. 38-39, March 2023.
Paper # MI2022-80 
Date of Issue 2023-02-27 (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 MI2022-80

Conference Information
Committee MI  
Conference Date 2023-03-06 - 2023-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWA SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2023-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improvement of Small Organ Accuracy in Multi-Organ Segmentation of Abdominal CT Images Using 2.5D Deformable Convolutional CNN 
Sub Title (in English)  
Keyword(1) CT images  
Keyword(2) Deep learning  
Keyword(3) Convolutional Neural Networks  
Keyword(4) 3D CT images  
Keyword(5) Computer-aided Detection Systems  
Keyword(6) Automatic recognition and detection of anatomical structures  
Keyword(7)  
Keyword(8)  
1st Author's Name Yuya Okumura  
1st Author's Affiliation University of Tsukuba (Univ of Tsukuba)
2nd Author's Name Hiroyuki Kudo  
2nd Author's Affiliation University of Tsukuba (Univ of Tsukuba)
3rd Author's Name Hotaka Takizawa  
3rd Author's Affiliation University of Tsukuba (Univ of Tsukuba)
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Speaker Author-1 
Date Time 2023-03-06 13:15:00 
Presentation Time 13 minutes 
Registration for MI 
Paper # MI2022-80 
Volume (vol) vol.122 
Number (no) no.417 
Page pp.38-39 
#Pages 2 
Date of Issue 2023-02-27 (MI) 

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