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Paper Abstract and Keywords
Presentation 2019-01-23 11:40
Influence of group normalization in multi-class organ segmentation of abdominal CT volumes
Chen Shen (Nagoya Univ.), Fausto Milletari, Holger R. Roth (Nvidia), Hirohisa Oda, Masahiro Oda, Yuichiro Hayashi (Nagoya Univ.), Kazunari Misawa (Aichi Cancer Center Hospital), Kensaku Mori (Nagoya Univ.) MI2018-94
Abstract (in Japanese) (See Japanese page) 
(in English) Organ segmentation is one of the most important branches of medical image analysis. Fully convolutional networks (FCNs) have become the dominant approach for this task and achieved considerable improvements for automated organ segmentation in volumetric image data, such as computed tomography images. In this paper, we investigate the influence of group normalization (GN) in multi-class organ segmentation from 3D CT volumes using fully convolutional network. Batch normalization is widely utilized in deep learning based methods to accelerate the convergence, reduce the reliance on initial learning rate and avoid overfitting. However, this type of normalization is strongly related to the batch size. Here, we study the influence of GN which is independent from batch size. In this research, we performed experiments on 377 cases of portal vein-phase abdominal CT volumes. The segmentation performance for small organs like artery and pancreas improved by introducing GN.
Keyword (in Japanese) (See Japanese page) 
(in English) deep learning / multi-organ segmentation / group normalization / computed tomography / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 412, MI2018-94, pp. 143-148, Jan. 2019.
Paper # MI2018-94 
Date of Issue 2019-01-15 (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)
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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) Influence of group normalization in multi-class organ segmentation of abdominal CT volumes 
Sub Title (in English)  
Keyword(1) deep learning  
Keyword(2) multi-organ segmentation  
Keyword(3) group normalization  
Keyword(4) computed tomography  
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1st Author's Name Chen Shen  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Fausto Milletari  
2nd Author's Affiliation Nvidia (Nvidia)
3rd Author's Name Holger R. Roth  
3rd Author's Affiliation Nvidia (Nvidia)
4th Author's Name Hirohisa Oda  
4th Author's Affiliation Nagoya University (Nagoya Univ.)
5th Author's Name Masahiro Oda  
5th Author's Affiliation Nagoya University (Nagoya Univ.)
6th Author's Name Yuichiro Hayashi  
6th Author's Affiliation Nagoya University (Nagoya Univ.)
7th Author's Name Kazunari Misawa  
7th Author's Affiliation Aichi Cancer Center Hospital (Aichi Cancer Center Hospital)
8th Author's Name Kensaku Mori  
8th Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker Author-1 
Date Time 2019-01-23 11:40:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # MI2018-94 
Volume (vol) vol.118 
Number (no) no.412 
Page pp.143-148 
#Pages 6 
Date of Issue 2019-01-15 (MI) 

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