MaskGen通过融合图像强度与稳定表征，提升3D医学图像分割的域泛化能力。
Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It
- 利用源域图像强度与域稳定表征联合训练，策略简洁且理论完备。
- 在多临床场景下实现全监督与少样本分割的显著性能提升。
- 兼容主流增强流程，易实现且适用于任意解剖区域。
我们提出MaskGen，一种理论严谨且设计简洁的3D生物医学图像分割域泛化方法。现代分割模型在模态、疾病严重程度、临床机构等变化下性能急剧下降，限制了其可靠应用。现有方法依赖极端数据增强、人工设计的域统计混合或架构重构，导致实现复杂且性能不稳定。MaskGen采用轻量级的联合学习策略，同时利用源域图像强度与域稳定的基础模型表征，训练鲁棒的分割模型。结果表明，该方法在多种临床场景下均显著提升了全监督和少样本分割性能。不同于以往方法，MaskGen不依赖特定架构或损失函数，兼容标准增强流程，易于实现，且可处理任意解剖区域。代码已开源：https://github.com/sebodiaz/MaskGen。
原文摘要 · Abstract (English)
We present MaskGen, a theoretically grounded and deliberately simple approach for domain generalization in 3D biomedical image segmentation. Modern segmentation models degrade sharply under shifts in modality, disease severity, clinical sites, and more, limiting their reliable adoption. Existing generalization methods address this using extreme augmentations, hand-engineered domain statistics mixing, or architectural redesigns that add significant implementation overhead while yielding inconsistent performance across biomedical settings. MaskGen instead presents a principled learning strategy with marginal overhead that utilizes both source-domain image intensities and domain-stable foundation model representations to train robust segmentation models. As a result, MaskGen achieves strong gains in both fully supervised and few-shot segmentation across broad clinical shifts in biomedical studies. Unlike prior approaches, MaskGen is architecture- and loss-agnostic, compatible with standard augmentation pipelines, easy to implement, and tackles arbitrary anatomical regions. Its implementation is freely available at https://github.com/sebodiaz/MaskGen.
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