不同工具计算医学图像分割距离指标差异巨大，可能误导研究结论。
Understanding implementation pitfalls of distance-based metrics for image segmentation
- 系统分析11个开源工具的实现逻辑，揭示算法细节差异
- 发现最远距离误差超100毫米，工具间结果存在显著统计差异
- 提醒研究者注意工具选择影响，尤其在医疗设备研发中
距离型度量（如豪斯多夫距离，HD）广泛用于（生物）医学图像分割性能评估。然而其实现复杂，开源工具间的关键差异长期未被社区识别，导致基准测试失真、生物标志物计算偏倚，并可能影响医疗器械开发与临床验证。本研究系统剖析11个开源工具的计算流程，通过概念分析与二维、三维图像数据集的实证对比发现：豪斯多夫距离误差可达100毫米以上，且工具间存在多个统计显著差异——同一组分割结果仅因选择不同实现即可呈现显著提升。这质疑了以往未考虑度量实现差异的研究对比的有效性。为此，我们提出工具选型建议，并为未来开源工具的实现演化提供参考。
原文摘要 · Abstract (English)
Distance-based metrics, such as the Hausdorff distance (HD), are widely used to validate segmentation performance in (bio)medical imaging. However, their implementation is complex, and critical differences across open-source tools remain largely unrecognized by the community. These discrepancies undermine benchmarking efforts, introduce bias in biomarker calculations, and potentially distort medical device development and clinical commissioning. In this study, we systematically dissect 11 open-source tools that implement distance-based metric computation by performing both a conceptual analysis of their computational steps and an empirical analysis on representative two- and three-dimensional image datasets. Alarmingly, we observed deviations in HD exceeding 100 mm and identified multiple statistically significant differences between tools - demonstrating that statistically significant improvements on the same set of segmentations can be achieved simply by selecting a particular implementation. These findings cast doubts on the validity of prior comparisons of results across studies without accounting for the differences in metric implementations. To address this, we provide practical recommendations for tool selection; additionally, our conceptual analysis informs about the future evolution of implementing open-source tools.
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