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 2022-07-08 16:20
[Short Paper] Multi-phase CT Image Segmentation with Single-Phase Annotation Using Adversarial Unsupervised Domain Adaptation
Swathi Ananda, Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua HAN (Yamaguchi Univ.), Lanfen Lin, Hongjie Hu (Zhejiang Univ.), Yen-Wei Chen (Ritsumeikan Univ.) MI2022-38
Abstract (in Japanese) (See Japanese page) 
(in English) Multi-phase computed tomography (CT) images are widely used for the diagnosis of liver disease, since different phase has different contrast enhancement (i.e., different domain), the multi-phase CT images should be annotated for all phase images for liver or tumor segmentation, which is a time-consuming and labor-expensive task. To lower the cost of manual annotation and domain shift problem, we propose an adversarial unsupervised domain adaptation (UDA) method for liver segmentation of multi-phase CT images with only single-phase annotation. The framework consists of two modules: a generator and a discriminator. We have employed U-Net as a generator as it is designed for medical image segmentation. We first use the annotated source images to train the generator only. Then we use the adversarial learning to train both generator and discriminator to minimize the difference between the source heatmap and target heatmap (domain shift). The refined generator is used for multi-phase CT image segmentation. To perform liver segmentation, initially, we have conducted experiments within each phase of our internal MPCT-FLL dataset (i.e., by utilizing the PV phase as the source and the ART phase as the target). Further, we use the publicly available LiTS dataset which consists of annotated PV phase images as the source domain, and each phase of our internal MPCT-FLL dataset i.e., (PV, ART, NC phase) as a target domain. The experimental results of this work suggest consistent and comparable improvement in the performance of our liver tumor segmentation over the previously reported state-of-the-art methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Multi-phase CT image / domain adaptation / adversarial learrning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 98, MI2022-38, pp. 24-25, July 2022.
Paper # MI2022-38 
Date of Issue 2022-07-01 (MI) 
ISSN Online edition: ISSN 2432-6380
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 MI2022-38

Conference Information
Committee MI  
Conference Date 2022-07-08 - 2022-07-09 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical imaging, recoginition, etc. 
Paper Information
Registration To MI 
Conference Code 2022-07-MI 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Multi-phase CT Image Segmentation with Single-Phase Annotation Using Adversarial Unsupervised Domain Adaptation 
Sub Title (in English)  
Keyword(1) Multi-phase CT image  
Keyword(2) domain adaptation  
Keyword(3) adversarial learrning  
1st Author's Name Swathi Ananda  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Yutaro Iwamoto  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
3rd Author's Name Xianhua HAN  
3rd Author's Affiliation Yamaguchi University, Japan (Yamaguchi Univ.)
4th Author's Name Lanfen Lin  
4th Author's Affiliation Zhejiang University, Hangzhou, China (Zhejiang Univ.)
5th Author's Name Hongjie Hu  
5th Author's Affiliation Zhejiang University, Hangzhou, China (Zhejiang Univ.)
6th Author's Name Yen-Wei Chen  
6th Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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 2022-07-08 16:20:00 
Presentation Time 20 minutes 
Registration for MI 
Paper # MI2022-38 
Volume (vol) vol.122 
Number (no) no.98 
Page pp.24-25 
Date of Issue 2022-07-01 (MI) 

[Return to Top Page]

[Return to IEICE Web Page]

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