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Paper Abstract and Keywords
Presentation 2018-09-21 09:40
[Short Paper] Automatic Segmentation of Epicardial Using Deep Learning
Ziyu Zhao, Tomoe Otoishi, Yutaro Iwamoto (Ritsumei Univ), Youji Tetsuka, Yuki Okada, Kiyosumi Maeda, Atsuyuki Wada, Atsunori Kashiwagi (Kusatsu General Hospita), Yanwei Chen (Ritsumei Univ) PRMU2018-55 IBISML2018-32
Abstract (in Japanese) (See Japanese page) 
(in English) The epicardial is a wall sac containing the heart and the roots of the great vessels. Epicardial adipose tissue adhere to the inside and outside of the epicardial, it is necessary to extract the epicardial to distinguish these fat tissues. A major challenge in epicardial segmentation is that in the cardiac Computed Tomography(CT) images, the epicardial exists as a very thin line and there are places where it can not be observed. Up to now, the main method of epicardial segmentation is manual extraction by experts. In this study, we propose a fully automatic method for epicardial segmentation, which is developed using U-Net, and demonstrated that it is possible to automatically segment epicardial.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep learning / Epicardial segmentation / U-Net / / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 219, PRMU2018-55, pp. 131-132, Sept. 2018.
Paper # PRMU2018-55 
Date of Issue 2018-09-13 (PRMU, IBISML) 
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 PRMU IBISML IPSJ-CVIM  
Conference Date 2018-09-20 - 2018-09-21 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To PRMU 
Conference Code 2018-09-PRMU-IBISML-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automatic Segmentation of Epicardial Using Deep Learning 
Sub Title (in English)  
Keyword(1) Deep learning  
Keyword(2) Epicardial segmentation  
Keyword(3) U-Net  
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1st Author's Name Ziyu Zhao  
1st Author's Affiliation Ritsumei University (Ritsumei Univ)
2nd Author's Name Tomoe Otoishi  
2nd Author's Affiliation Ritsumei University (Ritsumei Univ)
3rd Author's Name Yutaro Iwamoto  
3rd Author's Affiliation Ritsumei University (Ritsumei Univ)
4th Author's Name Youji Tetsuka  
4th Author's Affiliation Kusatsu General Hospital (Kusatsu General Hospita)
5th Author's Name Yuki Okada  
5th Author's Affiliation Kusatsu General Hospital (Kusatsu General Hospita)
6th Author's Name Kiyosumi Maeda  
6th Author's Affiliation Kusatsu General Hospital (Kusatsu General Hospita)
7th Author's Name Atsuyuki Wada  
7th Author's Affiliation Kusatsu General Hospital (Kusatsu General Hospita)
8th Author's Name Atsunori Kashiwagi  
8th Author's Affiliation Kusatsu General Hospital (Kusatsu General Hospita)
9th Author's Name Yanwei Chen  
9th Author's Affiliation Ritsumei University (Ritsumei Univ)
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Speaker Author-1 
Date Time 2018-09-21 09:40:00 
Presentation Time 10 minutes 
Registration for PRMU 
Paper # PRMU2018-55, IBISML2018-32 
Volume (vol) vol.118 
Number (no) no.219(PRMU), no.220(IBISML) 
Page pp.131-132 
#Pages 2 
Date of Issue 2018-09-13 (PRMU, IBISML) 

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