Paper Abstract and Keywords |
Presentation |
2021-07-08 13:30
[Short Paper]
Performance comparison of multiple deep CNN methods for multiple organ detection in CT images Daiki Kanoh, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita (Gifu Univ.) MI2021-10 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
The scheme of automatically recognizing multiple organs and detecting their localizations in 3D CT images is required for computer-aided diagnosis systems to support doctors' diagnosis. In this study, we compared the performance of our proposed method with three conventional object detection methods for recognition and detection of multiple organs in 3D CT images based on 2D deep CNNs. The proposed method is an improved version of Single Shot MultiBox Detector (SSD). We compared the performance of the proposed method to three conventional object detection methods: SSD and YOLOv3, which are widely used in the field of natural images based on 2D CNNs, and Detection Transformer (DETR), which uses a transformer and has attracted much attention recently. We applied those detection methods to the automatic recognition and detection of 17 organ types in 240 CT cases from a shared database of the research projector “computational anatomy”, in which contrast and non-contrast CT images are mixed. The experimental results demonstrate the effectiveness and challenges of the proposed method for organ detection in 3D CT images. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
3D CT Image / Detection / SSD / YOLOv3 / Transformer / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 98, MI2021-10, pp. 7-10, July 2021. |
Paper # |
MI2021-10 |
Date of Issue |
2021-07-01 (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) |
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MI2021-10 |
Conference Information |
Committee |
MI |
Conference Date |
2021-07-08 - 2021-07-09 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Medical imaging, physics, and recognition |
Paper Information |
Registration To |
MI |
Conference Code |
2021-07-MI |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Performance comparison of multiple deep CNN methods for multiple organ detection in CT images |
Sub Title (in English) |
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Keyword(1) |
3D CT Image |
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Detection |
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SSD |
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YOLOv3 |
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Transformer |
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1st Author's Name |
Daiki Kanoh |
1st Author's Affiliation |
Gifu University (Gifu Univ.) |
2nd Author's Name |
Xiangrong Zhou |
2nd Author's Affiliation |
Gifu University (Gifu Univ.) |
3rd Author's Name |
Takeshi Hara |
3rd Author's Affiliation |
Gifu University (Gifu Univ.) |
4th Author's Name |
Hiroshi Fujita |
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Gifu University (Gifu Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-07-08 13:30:00 |
Presentation Time |
30 minutes |
Registration for |
MI |
Paper # |
MI2021-10 |
Volume (vol) |
vol.121 |
Number (no) |
no.98 |
Page |
pp.7-10 |
#Pages |
4 |
Date of Issue |
2021-07-01 (MI) |
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