CRISP：通过迭代挤压过程进行约束细化，以实现域转移下的稳健医学图像分割
CRISP: Constrained Refinement via Iterative Squeezing Process for Robust Medical Image Segmentation under Domain Shift
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中文总结 AI 辅助
针对医学成像域转移问题，提出CRISP模型无关框架，利用“正区域的秩稳定性”假设，通过潜在特征扰动获取双先验并递归细化，经迭代训练框架提升分割精度，在多中心实验中显著优于现有方法。
中文摘要 AI 辅助
医学成像中的分布转移仍然是医学人工智能临床转化的核心瓶颈。现有域适应方法存在局限性。本文采用“正区域的秩稳定性”假设，提出CRISP框架，它无需测试时参数更新和目标域数据。通过潜在特征扰动获得双先验并递归细化，设计迭代训练框架。在多中心心脏MRI和基于CT的肺血管分割评估中，CRISP表现出卓越稳健性，显著优于现有方法。
英文摘要
Distribution shift in medical imaging remains a central bottleneck for the clinical translation of medical AI. Failure to address it can lead to severe performance degradation in unseen environments and exacerbate health inequities. Existing methods for domain adaptation are inherently limited by exhausting predefined possibilities through simulated shifts or pseudo-supervision. Such strategies struggle in the open-ended and unpredictable real world, where distribution shifts are effectively infinite. To address this challenge, we adopt the "Rank Stability of Positive Regions" as a working assumption under distribution shift, and use it to derive robust spatial hints for source-only segmentation. Guided by this assumption, we propose CRISP, a model-agnostic framework that, unlike deployment-time adaptation, requires no test-time parameter updates and no target-domain data--a target-free, plug-in refinement framework that segments with frozen weights. Rather than using ranking to directly output masks, CRISP exploits the stability of probability rankings under distribution shift to derive robust spatial priors. Via latent feature perturbation, perturbation-invariant high-grade regions define a high-precision (HP) core, while voxels that remain potentially foreground under at least one perturbation define a high-recall (HR) support; these dual priors are then recursively refined under perturbation. We then design an iterative training framework that progressively squeezes HP and HR toward the final segmentation. Extensive evaluations on multi-center cardiac MRI and CT-based lung vessel segmentation demonstrate CRISP's superior robustness, significantly outperforming state-of-the-art methods with striking HD95 reductions of up to 0.14 (7.0% improvement), 1.90 (13.1% improvement), and 8.39 (38.9% improvement) pixels across multi-center, demographic, and modality shifts, respectively.
