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Paper Abstract and Keywords
Presentation 2021-03-16 09:30
Statistical modeling of pulmonary vasculatures in CT volumes using a deep generative model
Yuki Saeki, Atshushi Saito (TUAT), Jean Cousty, Yukiko Kenmochi (LIGM/ UGE/ CNRS/ ESIEE Paris), Akinobu Shimizu (TUAT) MI2020-65
Abstract (in Japanese) (See Japanese page) 
(in English) The purpose of this study is to build a statistical intensity model of pulmonary vasculatures in CT volumes. In this study, we propose a modeling method that incorporates topological data analysis into the training of deep generative model. Specifically, we optimize the deep generative model by introducing a loss function that makes the topological features of the data generated by the model during training closer to the anatomically correct topological features of blood vessels. By using this method, we aim to build an end-to-end topology-preserving model.
Keyword (in Japanese) (See Japanese page) 
(in English) statistical model / topological data analysis / variational autoencoder / vasculature / pulmonary CT volume / deep generative model / anatomical prior / intensity distribution  
Reference Info. IEICE Tech. Rep., vol. 120, no. 431, MI2020-65, pp. 80-81, March 2021.
Paper # MI2020-65 
Date of Issue 2021-03-08 (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 2021-03-15 - 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) Statistical modeling of pulmonary vasculatures in CT volumes using a deep generative model 
Sub Title (in English)  
Keyword(1) statistical model  
Keyword(2) topological data analysis  
Keyword(3) variational autoencoder  
Keyword(4) vasculature  
Keyword(5) pulmonary CT volume  
Keyword(6) deep generative model  
Keyword(7) anatomical prior  
Keyword(8) intensity distribution  
1st Author's Name Yuki Saeki  
1st Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
2nd Author's Name Atshushi Saito  
2nd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
3rd Author's Name Jean Cousty  
3rd Author's Affiliation LIGM, Univ Gustave Eiffel, CNRS, ESIEE Paris, F-77454 Marne-la-Vallée, France (LIGM/ UGE/ CNRS/ ESIEE Paris)
4th Author's Name Yukiko Kenmochi  
4th Author's Affiliation LIGM, Univ Gustave Eiffel, CNRS, ESIEE Paris, F-77454 Marne-la-Vallée, France (LIGM/ UGE/ CNRS/ ESIEE Paris)
5th Author's Name Akinobu Shimizu  
5th Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
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Speaker Author-1 
Date Time 2021-03-16 09:30:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # MI2020-65 
Volume (vol) vol.120 
Number (no) no.431 
Page pp.80-81 
#Pages
Date of Issue 2021-03-08 (MI) 


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