Diffusion-driven Cycle-consistent Domain Adaptation for Cross-modality Medical Image Segmentation

Medical image segmentation often suffers from performance degradation when applied to images from different domains. To address this, we propose DiMA-Seg (Diffusion Model Adaptation for Segmentation), a novel framework for unsupervised domain adaptation in medical image segmentation. DiMA-Seg combines GAN-based image translation with diffusion model-based feature extraction, leveraging the strengths of both approaches. Our method utilizes the hierarchical nature of diffusion models to extract multi-scale features for accurate segmentation in the target domain. Experiment on MMWHS dataset demonstrates that DiMA-Seg outperforms existing methods in segmentation accuracy.