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| Enhanced Target Domain Representation BasedUnsupervised Cross-Domain Medical Image Segmentation |
| LIU Kai1, LU Runuo1, ZHENG Xiaorou1, DONG Shoubin1 |
| 1. Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641 |
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Abstract Medical images produced by different imaging modality devices exhibit varying degrees of distribution differences. Unsupervised domain adaptation methods typically aim to generalize models trained in the source domain to the unlabeled target domain by minimizing these distribution differences and using shared features between the source and target domains for result prediction. However, they often neglect the private features of the target domain. To address this issue, a method for enhanced target domain representation based unsupervised cross-domain medical image segmentation(TreUCMIS) is proposed in this paper. First, TreUCMIS acquires common features through shared feature learning, and a target domain feature encoder is trained through image reconstruction to capture the complete features of the target domain. Second, unsupervised self-training of the target domain strengthens the shared characteristics of deep and shallow features. Finally, the predicted results obtained from the shared and complete features are aligned, enabling the model to utilize the complete features of the target domain for segmentation and thus improving the generalization in the target domain. Experiments on two medical image segmentation datasets involving bidirectional domain adaptation tasks with CT and MRI(abdominal and cardiac datasets) demonstrate the effectiveness and superiority of TreUCMIS.
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Received: 15 August 2024
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| Fund:National Natural Science Foundation of China(No.61976239), Innovation Fund of High-End Scientific Research Institutions of Zhongshan City(No.2019AG031) |
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Corresponding Authors:
DONG Shoubin, Ph.D., professor. Her research interests include high performance computing, bioinformatics and biocomputing, and natural language processing.
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| About author:: ZHENG Xiaorou, Ph.D., assistant professor. Her research interests include hyperspectral remote sensing imagery, semi-supervised learning and cross-domain transfer learning. LU Runuo, Master student. Her research interests include computer vision and remote sensing imagery. LIU Kai, Master student. His research interests include deep learning, computer vision and image recognition. |
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2024, Vol. 37 
