IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

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)  
Keyword(1) deep learning  
Keyword(2) small-sample-size  
Keyword(3) medical image  
Keyword(4) semantic segmentation  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
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  
4th Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
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
Date of Issue 2022-02-14 (ITS, IE) 


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan