A Methodology for Clinically Driven Interactive Segmentation Evaluation

doi:10.1007/978-3-032-08970-0_2

2026-01-01

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DOI：10.1007/978-3-032-08970-0_2

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交互式分割是一种构建稳健、泛化性强的体素医学图像分割算法的有前景策略。然而，不一致且临床不现实的评估阻碍了公平比较，并歪曲了真实世界性能。我们提出了一种以临床为基础的方法来定义评估任务和指标，并构建了一个用于构建标准化评估流水线的软件框架。我们评估了最先进的算法在异构和复杂任务上的表现，并观察到：（i）在处理用户交互时最小化信息损失对模型稳健性至关重要；（ii）自适应缩放机制增强了稳健性并加速收敛；（iii）如果验证提示行为/预算与训练不同，性能会下降；（iv）2D方法在处理板状图像和粗略目标时表现良好，但3D上下文有助于处理大型或不规则形状的目标；（v）非医学领域模型（如SAM2）在对比度差和形状复杂时性能会下降。

Keyword:

Validation

Interactive Segmentation

Data Annotation

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HUMAN-AI COLLABORATION, HAIC 2025

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