ThreshGuide：用于半监督三维腹部多器官分割的类感知标签引导阈值化
ThreshGuide: Class-Aware Labeled-Guided Thresholding for Semi-Supervised 3D Abdominal Multi-Organ Segmentation
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中文总结 AI 辅助
提出ThreshGuide类感知阈值自适应框架，利用标注数据指导半监督腹部多器官分割的伪标签选择，在FLARE2022和AMOS2022上提升难学习器官的分割性能。
中文摘要 AI 辅助
伪标签是半监督医学图像分割的一种强大范式，但其有效性对置信度阈值高度敏感。在腹部多器官分割中，固定的全局阈值尤其次优，因为不同器官类别在大小、外观和学习难度上差异显著。在本工作中，我们提出ThreshGuide，一种类感知阈值自适应框架，利用标注数据指导未标注数据上的伪标签选择。该框架基于标准的教师-学生架构，教师模型在训练期间评估标注样本，通过最大化平衡精确度与覆盖率的误差感知Fβ准则来估计类感知阈值目标。随后，这些目标通过指数移动平均（EMA）进行平滑，并以类别依赖的方式用于过滤未标注体素。在FLARE2022和AMOS2022上的实验表明，ThreshGuide整体表现具有竞争力，尤其在难以学习的器官上带来了明显改进。
英文摘要
Pseudo-labeling is a strong paradigm for semi-supervised medical image segmentation, yet its effectiveness is highly sensitive to confidence thresholding. In abdominal multi-organ segmentation, a fixed global threshold is particularly suboptimal because organ classes differ substantially in size, appearance, and learning difficulty. In this work, we propose ThreshGuide, a class-aware threshold adaptation framework that uses labeled data to guide pseudo-label selection on unlabeled data. Built upon a standard teacher-student architecture, the teacher model evaluates labeled samples during training to estimate class-aware threshold targets by maximizing an error-aware F\b{eta} criterion that balances precision and coverage. These targets are then smoothed with an exponential moving average (EMA) and used to filter unlabeled voxels in a class-dependent manner. Experiments on FLARE2022 and AMOS2022 show that ThreshGuide performs competitively overall, yielding clear improvements specifically on hard-to-learn organs.
发表机构
- School of Intelligent Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院智能科学与技术学院）
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