Unsupervised Domain Adaptive Medical Segmentation Network Based on Contrastive Learning
Siqi Wang, Hao Wu, Xiaosheng Yu, Chengdong Wu · International Journal of Imaging Systems and Technology · 2025
ABSTRACT Accurate organ segmentation from magnetic resonance imaging (MRI) or computed tomography (CT) images is essential for surgical planning and decision‐making. Traditional fully supervised deep learning methods often exhibit a significant decline in performance when applied to datasets that differ from the training data, thus limiting their clinical applicability. This study proposes a novel segmentation method based on unsupervised domain adaptation, aiming to improve cross‐domain segmentation performance without the need for ground truth labels in the target domain. Specifically, our method trains the network with labeled source images and unlabeled target images, introducing a bidirectional feature‐prototype contrastive loss to align features across domains, minimizing within‐class variations and maximizing between‐class variations. To further improve model performance, we propose a prototype‐guided pseudo‐label fusion module that generates high‐quality pseudo‐labels for the unlabeled target images between domain prototypes. Experimental results show that our method outperforms other unsupervised domain adaptation segmentation approaches, achieving state‐of‐the‐art performance. Code is available at: https://github.com/WANGSIQII/UDA.git .