解决图像分割距离度量的实现误差问题，提升评估精度。
MeshMetrics: A Precise Implementation of Distance-Based Image Segmentation Metrics
- 基于网格的精确距离计算框架，避免传统方法的离散化偏差
- 同一分割结果下，豪斯多夫距离误差可超100mm，归因于实现差异
- 适合需要高精度评估的研究者，尤其关注可复现性的论文工作
图像分割研究快速发展，但随之暴露出可复现性危机。其中性能评估中的指标选择与实现方式至关重要。尽管近年在指标选择上已有改进，但实现可靠性长期被忽视。距离类指标的实现缺陷会导致开源工具间显著差异：例如同一对分割结果的豪斯多夫距离差异超过100 mm，归一化表面距离差异达30%pt。为此，我们提出MeshMetrics——一种基于网格的框架，相比传统的栅格方法，能更精确地计算距离类指标。通过理论分析与实证验证，我们证明MeshMetrics在准确性和精度上优于现有工具，且显著降低距离量化等离散化伪影的影响。代码已开源，地址为 https://github.com/gasperpodobnik/MeshMetrics。
原文摘要 · Abstract (English)
The surge of research in image segmentation has yielded remarkable performance gains but also exposed a reproducibility crisis. A major contributor is performance evaluation, where both selection and implementation of metrics play critical roles. While recent efforts have improved the former, the reliability of metric implementation has received far less attention. Pitfalls in distance-based metric implementation can lead to considerable discrepancies between common open-source tools, for instance, exceeding 100 mm for the Hausdorff distance and 30%pt for the normalized surface distance for the same pair of segmentations. To address these pitfalls, we introduce MeshMetrics, a mesh-based framework that provides a more precise computation of distance-based metrics than conventional grid-based approaches. Through theoretical analysis and empirical validation, we demonstrate that MeshMetrics achieves higher accuracy and precision than established tools, and is substantially less affected by discretization artifacts, such as distance quantization. We release MeshMetrics as an open-source Python package, available at https://github.com/gasperpodobnik/MeshMetrics.
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