Paper Info
Title
Dual Discriminator-Based Unsupervised Domain Adaptation Using Adversarial Learning for Liver Segmentation on Multiphase CT Images.
Abstract
Multiphase computed tomography (CT) images are widely used for the diagnosis of liver disease. Since each phase has different contrast enhancement (i.e., different domain), the multiphase CT images should be annotated for all phases to perform liver or tumor segmentation, which is a time-consuming and labor-expensive task. In this paper, we propose a dual discriminator-based unsupervised domain adaptation (DD-UDA) for liver segmentation on multiphase CT images without annotations. Our framework consists of three modules: a task-specific generator and two discriminators. We have performed domain adaptation at two levels: one is at the feature level, and the other is at the output level, to improve accuracy by reducing the difference in distributions between the source and target domains. Experimental results using public data (PV phase only) as the source domain and private multiphase CT data as the target domain show the effectiveness of our proposed DD-UDA method. Clinical relevance- This study helps to efficiently and accurately segment the liver on multiphase CT images, which is an important preprocessing step for diagnosis and surgical support. By using the proposed DD-UDA method, the segmentation accuracy has improved from 5%, 8%, and 6% respectively, for all phases of CT images with comparison to those without UDA.
Year
DOI
Venue
2022
10.1109/EMBC48229.2022.9871188
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DocType
Volume
ISSN
Conference
2022
2694-0604
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Swathi Ananda100.34
Yutaro Iwamoto202.03
Xian-Hua Han31410.19
Lanfen Lin47824.70
Hongjie Hu5119.50
Yen-Wei Chen6720155.73