Prior-Guided Selective Parameter Fine-Tuning for Source-Free Domain Adaptive Medical Image Segmentation

Source-free domain adaptation (SFDA) transfers knowledge from pre-trained source models to the unlabeled target domain without accessing the private source data. Conventional SFDA methods for medical image segmentation typically depend on pseudo-label driven self-training with full model fine-tuning. Although these methods have shown decent performance, the underlying principles remain insufficiently explored. In this work, we investigate SFDA through the PAC-Bayesian generalization error bound, demonstrating that its generalization error is jointly constrained by model complexity and pseudo-label noise. Motivated by this, we propose PATH, a selective PArameter fine-tuning framework guided by Topological and Historical priors for SFDA medical image segmentation. Specifically, PATH identifies domain-variant and task-distinctive parameters and sparsely updates them, thereby reducing effective model complexity during adaptation. In addition, PATH estimates pseudo-label reliability by integrating topological structure and historical prediction consistency priors to suppress pseudo-label noise. Extensive experiments on cross-scanner fundus image segmentation and cross-modality abdominal multi-organ segmentation benchmarks demonstrate that PATH outperforms competing SFDA methods, achieving state-of-the-art performance.