在医学图像分割中，何时一致性优于投票？统计标签融合的批判性分析
When Does Consensus Beat Voting? A Critical Analysis of Statistical Label Fusion in Medical Image Segmentation
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中文总结 AI 辅助
研究医学图像分割中一致性分割，通过推导数学基础并实验验证，发现常见条件下STAPLE存在问题，多数投票是强基线，深度一致性模型结合图像标签可处理问题，共形预测能保证不确定性，为评估方法提供基础。
中文摘要 AI 辅助
本文对一致性分割进行了严格且自包含的研究。我们从第一原理推导数学基础，包括生成模型、EM算法、范·莱姆普特的边缘化分析、可识别性条件、空间STAPLE和深度变分公式，并通过控制实验验证每个理论预测。核心发现令人清醒：在常见条件下，STAPLE退化为阈值化多数投票，EM次优性达95%，在类别不平衡时崩溃。多数投票是强大基线。深度一致性模型表明结合图像和标签时问题可处理，共形预测显示能实现形式上的不确定性保证。希望鼓励从业者批判性评估一致性方法，为更有原则的方法提供数学和实证基础。
英文摘要
This paper provides a rigorous, self-contained investigation of consensus segmentation. We derive the mathematical foundations from first principles -- the generative model, EM algorithm, Van Leemput's marginalization analysis, identifiability conditions, Spatial STAPLE, and deep variational formulations -- and validate each theoretical prediction through controlled experiments. The central finding is sobering: under common conditions, STAPLE reduces to thresholded majority voting, suffers 95% EM suboptimality, and collapses under class imbalance. These are not edge cases but typical scenarios in medical imaging. Majority voting -- simple, non-parametric, and robust -- is a surprisingly strong baseline that the field has perhaps too hastily dismissed in favor of more "sophisticated" methods. At the same time, the deep consensus model demonstrates that the consensus problem is not inherently difficult -- it becomes tractable when the image is used alongside the labels. And conformal prediction shows that formal uncertainty guarantees are achievable and practical. We hope this work encourages practitioners to critically evaluate their consensus methods rather than applying STAPLE by default, and provides the mathematical and empirical foundation for more principled approaches.
