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| Presentation | 2019-01-23 14:00 Study on Automated Labeling of Abdominal Arteries Using Machine Learning with Data Augmentation Yusuke Tetsumura, Yuichiro Hayashi, Masahiro Oda (Nagoya Univ), Takayuki Kitasaka (AIT), Kazunari Misawa (ACC), Kensaku Mori (Nagoya Univ)  MI2018-106 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this paper, we improve automated anatomical labeling accuracy for the abdominal arteries by introducing data augmentation technique. It is important to understand the blood vessel structure of patient accurately before or during surgery. Several blood vessel labeling methods using machine learning have been proposed. However, the automated labeling accuracy was low for cases having minor branching patterns in the hepatic arteries since the number of such cases is not enough for learning. In this paper, we artificially expanded number of cases having minor branching patterns and performed balancing of sample number. In our experiments, automated labeling accuracy for the hepatic arteries of cases having minor branching patterns was 85.7% on average while the previous method achieved 82.2%． | |
| 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. 118, no. 412, MI2018-106, pp. 191-196, Jan. 2019. | |
| Paper # | MI2018-106 | |
| Date of Issue | 2019-01-15 (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 | MI2018-106 |
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| Conference Information | |
| Committee | MI |
| Conference Date | 2019-01-22 - 2019-01-23 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical Image Engineering, Analysis, Recognition, etc. |
| Paper Information | |
| Registration To | MI |
| Conference Code | 2019-01-MI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Study on Automated Labeling of Abdominal Arteries Using Machine Learning with Data Augmentation |
| Sub Title (in English) | |
| Keyword(1) | blood vessel |
| Keyword(2) | CT volume |
| Keyword(3) | anatomical names recognition |
| Keyword(4) | blood vessel structures analysis |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Yusuke Tetsumura |
| 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 | Masahiro Oda |
| 3rd Author's Affiliation | Nagoya University (Nagoya Univ) |
| 4th Author's Name | Takayuki Kitasaka |
| 4th Author's Affiliation | Aichi Institute of Technology (AIT) |
| 5th Author's Name | Kazunari Misawa |
| 5th Author's Affiliation | Aichi Cancer Center Hospital (ACC) |
| 6th Author's Name | Kensaku Mori |
| 6th Author's Affiliation | Nagoya University (Nagoya Univ) |
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| Speaker | Author-1 |
| Date Time | 2019-01-23 14:00:00 |
| Presentation Time | 60 minutes |
| Registration for | MI |
| Paper # | MI2018-106 |
| Volume (vol) | vol.118 |
| Number (no) | no.412 |
| Page | pp.191-196 |
| #Pages | 6 |
| Date of Issue | 2019-01-15 (MI) |
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