CiSeg: Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation via Causal Intervention

Unsupervised domain adaptation (UDA) addresses the domain shift problem by transferring knowledge from labeled source domain data (e.g. CT) to unlabeled target domain data (e.g. MRI). While state-of-the-art methods reduce domain gaps via image- or feature-level alignment, their reliance on spurious correlations in the training data often limits generalization across domains. To overcome this limitation, we propose the Causal Intervention Segmentation Network (CiSeg), a novel framework that first integrates causal inference into UDA. A Structural Causal Model (SCM) is first constructed for the source domain to disentangle causal variables from bias variables, alleviating the impact of spurious correlations. Based on this SCM, we introduce a Counterfactual Disentanglement (CD) module to decompose the source domain's latent features into distinct causal and bias components, effectively eliminating their mutual dependencies. To enhance cross-domain consistency, two auxiliary components are introduced: Prototype-guided Contrastive Learning (PCL) and Causal-bias Residual Alignment (CBRA). PCL aligns pixel-level representations with their corresponding semantic prototypes, promoting stronger intra-class consistency and clearer inter-class separability. CBRA employs adversarial learning to align causal and bias residual features across domains, further enhancing feature-level invariance. Extensive experiments on cardiac, abdominal multi-organ, and BraTS18 segmentation tasks demonstrate that CiSeg outperforms state-of-the-art methods, achieving superior segmentation performance and robust cross-domain generalization. Code and models are available at https://github.com/lvpeiqing/CiSeg.