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Paper Abstract and Keywords
Presentation 2021-03-17 10:30
Study on automated anatomical labeling of abdominal arteries using Spectral-based Convolutional Graph Neural Networks
Yuta Hibi, Yuichiro Hayashi (Nagoya Univ), Takayuki Kitasaka (Aichi Institute of Tech), Hayato Itoh, Masahiro Oda (Nagoya Univ), Kazunari Misawa (Aichi Cancer Center Hospital), Kensaku Mori (Nagoya University/NII) MI2020-89
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we report an automated anatomical labeling method of abdominal arteries using Spectral-based Convolutional Graph Neural Networks. In laparoscopic surgery, which is widely performed today, it is difficult to understand the vascular structure due to the narrow field of laparoscope camera. Therefore, computer assistance is desired to help understanding of grasping vascular structure on surgeons by presenting the results of automated anatomical labeling of abdominal arteries. The use of a wide range of vascular features is important for learning vascular structures, and propose automated anatomical labeling of abdominal arteries by ChebNet that can handle a wide range of graph convolution. A maximum F value of 93.1% was achieved by introducing a weighted softmax cross entropy loss to reduce the imbalance in the data set.
Keyword (in Japanese) (See Japanese page) 
(in English) blood vessel / CT volume / anatomical names recognition / blood vessel structures analysis / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 431, MI2020-89, pp. 176-181, March 2021.
Paper # MI2020-89 
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)
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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) Study on automated anatomical labeling of abdominal arteries using Spectral-based Convolutional Graph Neural Networks 
Sub Title (in English)  
Keyword(1) blood vessel  
Keyword(2) CT volume  
Keyword(3) anatomical names recognition  
Keyword(4) blood vessel structures analysis  
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Keyword(6)  
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1st Author's Name Yuta Hibi  
1st Author's Affiliation Nagoya University (Nagoya Univ)
2nd Author's Name Yuichiro Hayashi  
2nd Author's Affiliation Nagoya University (Nagoya Univ)
3rd Author's Name Takayuki Kitasaka  
3rd Author's Affiliation Aichi Institute of Technology (Aichi Institute of Tech)
4th Author's Name Hayato Itoh  
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 Kazunari Misawa  
6th Author's Affiliation Aichi Cancer Center Hospital (Aichi Cancer Center Hospital)
7th Author's Name Kensaku Mori  
7th Author's Affiliation Nagoya University/National Institute of Informatics (Nagoya University/NII)
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Speaker Author-1 
Date Time 2021-03-17 10:30:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # MI2020-89 
Volume (vol) vol.120 
Number (no) no.431 
Page pp.176-181 
#Pages 6 
Date of Issue 2021-03-08 (MI) 

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