Federated Spatial Prior-Based Source-Free Domain Adaptation for White Matter Hyperintensities Segmentation

White matter hyperintensities (WMH) are important imaging biomarkers for cerebral small vessel disease, and their automatic segmentation across data with different distributions is crucial for assessing brain health and supporting diagnosis. However, cross-domain WMH segmentation remains challenging in privacy-sensitive and label-scarce clinical settings. Existing methods either relied on source domain data, violating privacy constraints, or lacked spatial guidance, which resulted in poor generalization, such as low sensitivity to small lesions. To address these challenges, we developed a source-free domain adaptation (SFDA) framework enhanced by federated spatial prior modeling. Our method used a dual-path pseudo-label generator that leveraged spatial priors to improve boundary accuracy and enhance the detection of small lesions. These priors were optimized via federated learning across multiple sites without sharing raw data, boosting model generalization while preserving privacy. The model was then fine-tuned using refined pseudo-labels. Experimental results demonstrated that our method consistently outperforms state-of-the-art UDA and SFDA methods, achieving 3-10% DSC improvement in most sites across 3 public and 7 private datasets. It also showed superior performance in small lesion detection and boundary delineation. Our method offered a robust, privacy-preserving solution for WMH segmentation and provided valuable support for early diagnosis and risk assessment of cerebrovascular diseases.