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 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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MI2020-65 |
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) |
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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 |
2 |
Date of Issue |
2021-03-08 (MI) |
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