Structured Semantic Consistency for Unsupervised Cross-Domain Nuclei Segmentation

Domain shifts in staining, tissue composition, and annotation conventions limit the transferability of nuclei instance segmentation models across histopathology datasets. Prediction consistency alone cannot ensure correct cell-type assignments, making semantic alignment a central challenge in unsupervised domain adaptation. To address this problem, we propose a structured semantic consistency framework that transfers a multi-head nuclei segmentation network from a labeled source domain to an unlabeled target domain. The framework combines source foreground preservation with masked target-view consistency, aligns reliable target features with supervised source-class prototypes, and regularizes inter-class relationships using fixed text embeddings. A class-support constraint is applied only when source and target annotation spaces differ. Training follows a two-stage strategy consisting of joint source–target pre-adaptation and subsequent supervised source refinement. Experiments cover internal domain transfers within PanNuke and cross-dataset adaptation from PanNuke to CoNSeP and Lizard. On CoNSeP and Lizard, class-aware panoptic quality rises from 0.1059 to 0.2335 and from 0.0733 to 0.1127. These results demonstrate the effectiveness of combining prediction consistency, prototype alignment, and semantic relational constraints for cross-domain nuclei instance segmentation without target-domain annotations.