Deep learning has shown remarkable progress in medical image semantic segmentation, yet its success heavily depends on large-scale expert annotations and consistent data distributions. In practice, annotations are scarce, and images are collected from multiple scanners or centers, leading to mixed-domain settings with unknown domain labels and severe domain gaps. Existing semi-supervised or domain adaptation approaches typically assume either a single domain shift or access to explicit domain indices, which rarely hold in real-world deployment. In this paper, we propose a domain-invariant mixed-domain semi-supervised segmentation framework that jointly enhances data diversity and mitigates domain bias. A Copy-Paste Mechanism (CPM) augments the training set by transferring informative regions across domains, while a Cluster Maximum Mean Discrepancy (CMMD) block clusters unlabeled features and aligns them with labeled anchors via an MMD objective, encouraging domain-invariant representations. Integrated within a teacher-student framework, our method achieves robust and precise segmentation even with very few labeled examples and multiple unknown domain discrepancies. Experiments on Fundus and M&Ms benchmarks demonstrate that our approach consistently surpasses semi-supervised and domain adaptation methods, establishing a potential solution for mixed-domain semi-supervised medical image segmentation.

翻译：深度学习在医学图像语义分割领域取得了显著进展，但其成功在很大程度上依赖于大规模专家标注和一致的数据分布。实际上，标注数据稀缺，且图像通常采集自多台扫描仪或多个中心，导致存在未知域标签和严重域差异的混合域场景。现有的半监督或域适应方法通常假设仅存在单一域偏移或能够获取明确的域索引，这些假设在现实部署中很少成立。本文提出一种域不变的混合域半监督分割框架，该框架能同时增强数据多样性并减轻域偏差。通过一种复制粘贴机制（CPM），在不同域间迁移信息丰富的区域以扩充训练集；同时，一个聚类最大均值差异（CMMD）模块对未标注特征进行聚类，并通过最大均值差异（MMD）目标将其与已标注锚点对齐，从而鼓励学习域不变表示。该方法集成于师生框架中，即使在标注样本极少且存在多个未知域差异的情况下，也能实现鲁棒且精确的分割。在Fundus和M&Ms基准数据集上的实验表明，我们的方法一致优于现有的半监督和域适应方法，为混合域半监督医学图像分割提供了一个潜在的解决方案。