Recently, prototype learning has emerged in semi-supervised medical image segmentation and achieved remarkable performance. However, the scarcity of labeled data limits the expressiveness of prototypes in previous methods, potentially hindering the complete representation of prototypes for class embedding. To address this problem, we propose the Mixed Prototype Consistency Learning (MPCL) framework, which includes a Mean Teacher and an auxiliary network. The Mean Teacher generates prototypes for labeled and unlabeled data, while the auxiliary network produces additional prototypes for mixed data processed by CutMix. Through prototype fusion, mixed prototypes provide extra semantic information to both labeled and unlabeled prototypes. High-quality global prototypes for each class are formed by fusing two enhanced prototypes, optimizing the distribution of hidden embeddings used in consistency learning. Extensive experiments on the left atrium and type B aortic dissection datasets demonstrate MPCL's superiority over previous state-of-the-art approaches, confirming the effectiveness of our framework. The code will be released soon.

通过混合原型一致性学习（MPCL）框架，我们提出了一种解决医学图像分割中标记数据稀缺性带来的原型表达不完整的问题的方法。在该框架中，平均教师生成标记和未标记数据的原型，而辅助网络通过CutMix处理混合数据产生额外的原型。通过原型融合，混合原型为标记和未标记的原型提供额外的语义信息。对左心房和B型主动脉夹层数据集的实验结果表明，MPCL优于先前的最先进方法，证实了我们框架的有效性。

混合原型一致性学习用于半监督医学图像分割