Robust unsupervised domain adaptation for medical image segmentation via frequency-conditioned graph diffusion

Abstract

Cross-domain variability in medical imaging, arising from differences in scanners, acquisition protocols, and patient populations, remains a major challenge for reliable semantic segmentation. Existing unsupervised domain adaptation (UDA) methods predominantly rely on image-level transformations or feature alignment, which often fail to preserve anatomical consistency under large domain shifts. In this work, we propose a novel structured latent UDA framework that performs domain alignment in a topology-aware representation space rather than directly modifying image appearance. Specifically, we introduce a Frequency-Conditioned Graph Diffusion paradigm, where convolutional features are transformed into anatomical graphs to explicitly capture structural relationships. A latent diffusion process then progressively refines these graph embeddings, guided by frequency-aware contextual cues, enabling robust cross-domain alignment. To further enhance generalization, we integrate structural consistency regularization with adversarial latent alignment, eliminating the need for labeled target data. A dedicated decoder reconstructs dense segmentation maps, while stochastic diffusion sampling provides uncertainty estimates for improved potential clinical reliability. Extensive experiments on multiple public medical imaging benchmarks demonstrate that our method consistently outperforms state-of-the-art UDA approaches, achieving superior segmentation accuracy and robustness under significant domain shifts. These results highlight the effectiveness of structured latent modeling and diffusion-based learning for robust domain-adaptive segmentation.