Enhanced Target Domain Representation Based Unsupervised Cross-Domain Medical Image Segmentation

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. © 2024 Science Press. All rights reserved.