提出考虑空间结构的分割不确定性评估方法，提升医学影像分析可信度。
Spatially-Aware Evaluation of Segmentation Uncertainty
- 引入结构与边界信息，改进传统独立体素评估方式
- 在前列腺分区分割挑战中验证，显著区分真实与虚假不确定性模式
- 帮助临床更准确识别不可靠区域，适用于医疗图像分析场景
不确定性图可标识分割预测中的不可靠区域。然而，现有评估指标大多将体素独立处理，忽视空间上下文和解剖结构，可能导致对质性不同的不确定性模式（如散点式与边界对齐式）赋予相同分数。本文提出三种融合结构与边界信息的时空感知评估指标，并在医学分割十项全能竞赛中的前列腺分区分割挑战数据上进行了全面验证。结果表明，新方法在与临床重要因素的对齐性及区分有意义与虚假不确定性模式方面均有显著提升。
原文摘要 · Abstract (English)
Uncertainty maps highlight unreliable regions in segmentation predictions. However, most uncertainty evaluation metrics treat voxels independently, ignoring spatial context and anatomical structure. As a result, they may assign identical scores to qualitatively distinct patterns (e.g., scattered vs. boundary-aligned uncertainty). We propose three spatially aware metrics that incorporate structural and boundary information and conduct a thorough validation on medical imaging data from the prostate zonal segmentation challenge within the Medical Segmentation Decathlon. Our results demonstrate improved alignment with clinically important factors and better discrimination between meaningful and spurious uncertainty patterns.
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