Cross-Modality Abdominal Multi-Organ Segmentation via Source-Free Unsupervised Domain Adaptation

Abstract

Cross-modality organ segmentation is crucial in medical imaging, enabling the use of annotated data from one modality (e.g., CT) to segment structures in another (e.g., MRI). However, differences between modalities and limited source data often hinder standard supervised or unsupervised domain adaptation methods. A novel Source-Free Unsupervised Domain Adaptation (SF-UDA) framework is proposed for abdominal multi-organ segmentation that does not require access to the original source labeled data during adaptation. Our two-stage approach first generates pseudo-labels with a pre-trained source model and reduces domain shifts using a contrastive cycle-based style-transfer module, while simultaneously training multiple networks with segmentation and entropy minimization losses to enhance feature alignment. In the second stage, segmentation results are refined through cycle-based learning and mask refinement to ensure spatial consistency and improve boundary accuracy. Experiments show our framework surpasses state-of-the-art SFUDA methods on abdominal multi-organ datasets, achieving a mean Dice coefficient of 0.9182.