Partially Supervised Multi-organ Segmentation via Affinity-Aware Consistency Learning and Cross Site Feature Alignment
Qin Zhou, Peng Liu & Guoyan Zheng
Partially Supervised Multi-Organ Segmentation (PSMOS) has attracted increasing attention. However, facing with challenges from lacking sufficiently labeled data and cross-site data discrepancy, PSMOS remains largely an unsolved problem. In this paper, to fully take advantage of the unlabeled data, we propose to incorporate voxel-to-organ affinity in embedding space into a consistency learning framework, ensuring consistency in both label space and latent feature space. Furthermore, to mitigate the cross-site data discrepancy, we propose to propagate the organ-specific feature centers and inter-organ affinity relationships across different sites, calibrating the multi-site feature distribution from a statistical perspective. Extensive experiments manifest that our method generates favorable results compared with other state-of-the-art methods, especially on hard organs with relatively smaller sizes.