Paper Abstract and Keywords |
Presentation |
2023-03-06 16:38
An Investigation of Effectiveness of Organ Features in Automated Anatomical Labeling Using Graph Neural Networks Tomoya Deguchi, Yuichiro Hayashi, Masahiro Oda (Nagoya Univ), Takayuki Kitasaka (Aichi Institute of Tech), Kazunari Misawa (Aichi Cancer Center Hospital), Kensaku Mori (Nagoya Univ/NII) MI2022-94 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this study, we investigate important organ features for automated anatomical labeling of abdominal arteries using Graph Neural Networks. It is important for physicians to understand the anatomical structures of their patients in diagnosis and treatment. On the other hand, abdominal blood vessel structures are complex and difficult to understand. Therefore, several researches have been conducted on automated labeling methods for anatomical name of abdominal arteries to assist physicians in understanding vessel structures. However, it is still difficult to label hepatic arteries and gastric arteries. In this study, we label blood vessel name by changing the organ features and comparing the results to investigate the organ features that are effective in automated anatomical labeling of abdominal arteries. |
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. 122, no. 417, MI2022-94, pp. 105-110, March 2023. |
Paper # |
MI2022-94 |
Date of Issue |
2023-02-27 (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 |
MI2022-94 |
|