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.

翻译：近年来，原型学习在半监督医学图像分割中崭露头角并取得了显著性能。然而，现有方法中标注数据的稀缺限制了原型的表达能力，可能阻碍原型对类别嵌入的完整表征。针对这一问题，我们提出了混合原型一致性学习（MPCL）框架，该框架包含一个均值教师网络和一个辅助网络。均值教师网络为标注数据与未标注数据生成原型，而辅助网络则为经CutMix处理的混合数据产生额外原型。通过原型融合，混合原型为标注原型和未标注原型提供了额外的语义信息。通过融合两个增强型原型，形成了每个类别的高质量全局原型，优化了用于一致性学习的隐层嵌入分布。在左心房和B型主动脉夹层数据集上的大量实验表明，MPCL优于先前的最新方法，证实了我们框架的有效性。代码将很快公开。