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| Presentation | 2022-07-09 10:30 Non-rigid registration method for longitudinal chest CT images in Covid-19 Yuma Iwao (QST), Naoko Kawata, Yuki Segiguchi, Hideaki Haneishi (Chiba Univ)  MI2022-41 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In time series analysis of the lungs, a non-rigid registration method that compensates for differences in respiratory status is needed. Recently, several accurate and fast methods using deep learning have been proposed, but their applicability to time series CT of COVID-19 patients with lesion site changes has not been tested. In this study, we propose a practical process to apply a deep learning method called Voxel Morph to time-series chest CT, and verify its robustness to the imaging features of lesions in COVID-19 patients. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Chest CT / Time series analysis / Non-rigid registration / Deep Learning / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 98, MI2022-41, pp. 34-38, July 2022. | |
| Paper # | MI2022-41 | |
| Date of Issue | 2022-07-01 (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) | |
| Download PDF | MI2022-41 |
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| Conference Information | |
| Committee | MI |
| Conference Date | 2022-07-08 - 2022-07-09 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical imaging, recoginition, etc. |
| Paper Information | |
| Registration To | MI |
| Conference Code | 2022-07-MI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Non-rigid registration method for longitudinal chest CT images in Covid-19 |
| Sub Title (in English) | |
| Keyword(1) | Chest CT |
| Keyword(2) | Time series analysis |
| Keyword(3) | Non-rigid registration |
| Keyword(4) | Deep Learning |
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| 1st Author's Name | Yuma Iwao |
| 1st Author's Affiliation | ational Institutes for Quantum Science and Technology (QST) |
| 2nd Author's Name | Naoko Kawata |
| 2nd Author's Affiliation | Chiba University (Chiba Univ) |
| 3rd Author's Name | Yuki Segiguchi |
| 3rd Author's Affiliation | Chiba University (Chiba Univ) |
| 4th Author's Name | Hideaki Haneishi |
| 4th Author's Affiliation | Chiba University (Chiba Univ) |
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| Speaker | Author-1 |
| Date Time | 2022-07-09 10:30:00 |
| Presentation Time | 20 minutes |
| Registration for | MI |
| Paper # | MI2022-41 |
| Volume (vol) | vol.122 |
| Number (no) | no.98 |
| Page | pp.34-38 |
| #Pages | 5 |
| Date of Issue | 2022-07-01 (MI) |
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