Reliable Multi-Prototypical Contrastive Learning for Semi-supervised Heterogeneous and Multi-organ Medical Image Segmentation

Accurate multi-organ segmentation across heterogeneous medical images is pivotal for real-world surgical navigation. The scarcity of annotation constitutes a well-established consensus in the field, prompting semi-supervised learning to emerge as a prominent solution. However, two critical bottlenecks persist in clinical translation: (1) inter-class feature ambiguity, and (2) high multi-source sample heterogeneity. To tackle these bottle-necks, we propose TP-Net, a semi-supervised framework for multi-organ segmentation in heterogeneous medical images, which innovatively integrates reliable multi-prototype contrastive learning. Specifically, we propose a heterogeneous prototype dynamic evolution mechanism that self-adaptively models fragmented intra-class distributions in multi-source data. Then, to mitigate prototype shift, we first devise an uncertainty-aware cross-domain alignment strategy grounded in the smoothness assumption, which constructs reliable prototypes by propagating reliable pixel prediction distributions from labeled to unlabeled domains. Furthermore, this is synergized with contrastive separation that enforces feature proximity to class-matched prototypes in the embedding space, effectively resolving inter-class ambiguity by minimizing overlap between adjacent organ clusters via prototype repulsion. Experimental results on two public datasets and one in-house dataset prove that the proposed method achieves state-of-the-art performance. Further, clinical validation with our self-developed surgical navigation system demonstrated the clinical viability of the proposed method. The code associated with this work will be made publicly available at https://github. com/JIESHUREN330/TP-Net/tree/main.