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Paper Abstract and Keywords
Presentation 2021-07-08 13:30
[Short Paper] Performance comparison of multiple deep CNN methods for multiple organ detection in CT images
Daiki Kanoh, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita (Gifu Univ.) MI2021-10
Abstract (in Japanese) (See Japanese page) 
(in English) The scheme of automatically recognizing multiple organs and detecting their localizations in 3D CT images is required for computer-aided diagnosis systems to support doctors' diagnosis. In this study, we compared the performance of our proposed method with three conventional object detection methods for recognition and detection of multiple organs in 3D CT images based on 2D deep CNNs. The proposed method is an improved version of Single Shot MultiBox Detector (SSD). We compared the performance of the proposed method to three conventional object detection methods: SSD and YOLOv3, which are widely used in the field of natural images based on 2D CNNs, and Detection Transformer (DETR), which uses a transformer and has attracted much attention recently. We applied those detection methods to the automatic recognition and detection of 17 organ types in 240 CT cases from a shared database of the research projector “computational anatomy”, in which contrast and non-contrast CT images are mixed. The experimental results demonstrate the effectiveness and challenges of the proposed method for organ detection in 3D CT images.
Keyword (in Japanese) (See Japanese page) 
(in English) 3D CT Image / Detection / SSD / YOLOv3 / Transformer / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 98, MI2021-10, pp. 7-10, July 2021.
Paper # MI2021-10 
Date of Issue 2021-07-01 (MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee MI  
Conference Date 2021-07-08 - 2021-07-09 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical imaging, physics, and recognition 
Paper Information
Registration To MI 
Conference Code 2021-07-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Performance comparison of multiple deep CNN methods for multiple organ detection in CT images 
Sub Title (in English)  
Keyword(1) 3D CT Image  
Keyword(2) Detection  
Keyword(3) SSD  
Keyword(4) YOLOv3  
Keyword(5) Transformer  
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1st Author's Name Daiki Kanoh  
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 2021-07-08 13:30:00 
Presentation Time 30 minutes 
Registration for MI 
Paper # MI2021-10 
Volume (vol) vol.121 
Number (no) no.98 
Page pp.7-10 
#Pages
Date of Issue 2021-07-01 (MI) 


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