Semi-supervised Segmentation Network Based on Prototype-Oriented Local Contrastive Learning for Pregnancy Tissue in MR Images.

Cesarean scar pregnancy (CSP) is a severe form of ectopic pregnancy, where early screening and monitoring are critical to reducing the risk of prolonged uterine bleeding and other serious complications. Accurate segmentation of pregnancy tissue plays a vital role in clinical assessment and treatment planning. However, the segmentation of pregnancy tissue is particularly challenging due to the diverse morphology and small size of target regions, which causes limited accuracy in existing studies. In addition, large-scale annotated datasets are lacking, and manual annotation is costly and time-consuming. To address these issues, we propose a prototype-oriented local contrastive learning framework for semi-supervised pregnancy tissue segmentation, which addresses the informatics challenges of limited labeled data and fine-grained feature extraction in medical image segmentation. Specifically, representative prototypes are first extracted to characterize the distribution of features in different images. Then, a prototype-guided local contrastive strategy is introduced to incorporate supervised signals into the contrastive learning process. This guides unlabeled data to align with supervised prototype centers, thereby improving segmentation accuracy. Experiments conducted on self-constructed pregnancy tissue dataset demonstrated that the proposed method achieved Dice coefficients of 86.91% at a 50% labeling rate. To further evaluate the generalizability of the method, we also validated it on the public cardiac dataset, achieving a Dice coefficient of 87.34%. These results not only advance semi-supervised learning in medical imaging informatics but also provide a reliable tool for accurate CSP tissue segmentation, supporting clinical decision-making in early ectopic pregnancy management.