Paper Abstract and Keywords |
Presentation |
2022-02-21 15:35
Liver Tumor Segmentation by Using a Massive-Training Artificial Neural Network (MTANN) and its Analysis in Liver CT. Yuqiao Yang, Muneyuki Sato, Ze Jin, Kenji Suzuki (Tokyo Tech) ITS2021-33 IE2021-42 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Based on a 3D massive-training artificial neural network (MTANN) combined with a Hessian-based ellipse enhancer, a small-sample-size deep learning technique for semantic segmentation of liver tumors in contrast-enhanced CT is proposed. To show the proposed model's efficiency in a small-sample size dataset, we trained the proposed models with only 7 tumors from 7 patients, and 14 tumors from 12 patients. The proposed model achieved a Dice score of 0.703 with the training set of 12 patients. The accuracy was comparable to the CNN-based method with 131 patients in the MICCAI 2017 competition. The proposed model is essential in deep learning applications in medical imaging where a large database is not available. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
deep learning / small-sample-size / medical image / semantic segmentation / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 374, IE2021-42, pp. 49-54, Feb. 2022. |
Paper # |
IE2021-42 |
Date of Issue |
2022-02-14 (ITS, IE) |
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 |
ITS2021-33 IE2021-42 |
Conference Information |
Committee |
IE ITS ITE-AIT ITE-ME ITE-MMS |
Conference Date |
2022-02-21 - 2022-02-22 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Image Processing, etc. |
Paper Information |
Registration To |
IE |
Conference Code |
2022-02-IE-ITS-AIT-ME-MMS |
Language |
English |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Liver Tumor Segmentation by Using a Massive-Training Artificial Neural Network (MTANN) and its Analysis in Liver CT. |
Sub Title (in English) |
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deep learning |
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small-sample-size |
Keyword(3) |
medical image |
Keyword(4) |
semantic segmentation |
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1st Author's Name |
Yuqiao Yang |
1st Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
2nd Author's Name |
Muneyuki Sato |
2nd Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
3rd Author's Name |
Ze Jin |
3rd Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
4th Author's Name |
Kenji Suzuki |
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Tokyo Institute of Technology (Tokyo Tech) |
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Speaker |
Author-1 |
Date Time |
2022-02-21 15:35:00 |
Presentation Time |
15 minutes |
Registration for |
IE |
Paper # |
ITS2021-33, IE2021-42 |
Volume (vol) |
vol.121 |
Number (no) |
no.373(ITS), no.374(IE) |
Page |
pp.49-54 |
#Pages |
6 |
Date of Issue |
2022-02-14 (ITS, IE) |
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