Counterfactual Logit Debiasing for Unsupervised Cross-Modality Segmentation

Unsupervised domain adaptation enables cardiac image segmentation across acquisition domains without target-domain annotations. Existing methods have improved cross-modality transfer through image translation, feature alignment, and prediction regularization, but acquisition-related context can still influence the final segmentation decision. We propose counterfactual logit debiasing (CLD), which compares predictions from observed spatial features and a structural reference under the same global domain context. Both predictions use a shared segmentation head, and their logit difference serves as the final segmentation output. CLD is trained with labeled source images and unlabeled target images. On bSSFP-to-LGE and CT-to-MR cardiac segmentation, it achieves mean Dice scores of 0.823 and 0.778, improving upon the strongest compared UDA baseline by 0.7 and 1.7 percentage points, respectively. Component studies further examine the contributions of the model design and prediction contrast.