提出新指标SCC，更精准评估图像分割中误差的空间分布。
A Novel Distance-Based Metric for Quality Assessment in Image Segmentation
- 基于结构表面附近误差的分布设计新度量方法
- 在合成与真实数据上均能有效区分表面与内部误差
- 指标直观易懂，适用于不同结构和数据集对比
分割质量评估在各类应用的分割方法开发、优化与比较中具有基础性作用。现有主流评价指标多依赖错误像素数量统计，未能捕捉误差的空间分布特征。传统距离型指标如平均豪斯多夫距离难以解释且跨方法、跨数据集比较困难。本文提出表面一致性系数（Surface Consistency Coefficient, SCC），一种新型距离型质量度量，通过量化误差靠近目标结构表面的程度来反映空间分布特性。利用合成数据与真实分割结果进行严格分析，验证了SCC在区分表面误差与内部误差方面的鲁棒性与有效性，同时具备良好的可解释性与跨结构可比性。
原文摘要 · Abstract (English)
The assessment of segmentation quality plays a fundamental role in the development, optimization, and comparison of segmentation methods which are used in a wide range of applications. With few exceptions, quality assessment is performed using traditional metrics, which are based on counting the number of erroneous pixels but do not capture the spatial distribution of errors. Established distance-based metrics such as the average Hausdorff distance are difficult to interpret and compare for different methods and datasets. In this paper, we introduce the Surface Consistency Coefficient (SCC), a novel distance-based quality metric that quantifies the spatial distribution of errors based on their proximity to the surface of the structure. Through a rigorous analysis using synthetic data and real segmentation results, we demonstrate the robustness and effectiveness of SCC in distinguishing errors near the surface from those further away. At the same time, SCC is easy to interpret and comparable across different structural contexts.
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