Family of boundary overlap metrics for the evaluation of medical image segmentation
Family of boundary overlap metrics for the evaluation of medical image segmentation
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DOI：
10.1117/1.jmi.5.1.015006
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发表时间：
2018-01-01
影响因子：
2.4
通讯作者：
Voiculescu, Irina
中科院分区：
文献类型：
--
作者：
Yeghiazaryan, Varduhi;Voiculescu, Irina
所有的医学图像分割算法都需要进行验证和比较，但没有一个评价框架在成像界被广泛接受。文献中流行的评估指标在对分割结果进行排名的方式上都不一致：它们往往对一种或另一种类型的分割错误（大小，位置和形状）敏感，但没有一个单一的指标涵盖所有错误类型。我们引入了一个家庭的指标，混合动力的特点。这些指标量化的相似性或差异的分割区域，通过考虑他们的平均重叠在固定大小的邻域点的边界上的这些区域。我们的指标比现有文献中的其他指标对分割错误类型的组合更敏感。我们比较的度量性能收集的分割结果来源于精心编制的二维合成数据和三维医学图像。我们表明，我们的指标：（1）成功地惩罚错误，特别是区域边界周围的错误;（2）当现有度量不一致时给出低相似性分数，从而避免过度膨胀的分数;以及（3）在更宽的值范围内对分割结果进行评分。我们分析了一个有代表性的指标，从这个家庭和它的自由参数对错误的敏感性和运行时间的影响。(C)2018年，摄影光学仪器工程师协会（SPIE）
All medical image segmentation algorithms need to be validated and compared, yet no evaluation framework is widely accepted within the imaging community. None of the evaluation metrics that are popular in the literature are consistent in the way they rank segmentation results: they tend to be sensitive to one or another type of segmentation error (size, location, and shape) but no single metric covers all error types. We introduce a family of metrics, with hybrid characteristics. These metrics quantify the similarity or difference of segmented regions by considering their average overlap in fixed-size neighborhoods of points on the boundaries of those regions. Our metrics are more sensitive to combinations of segmentation error types than other metrics in the existing literature. We compare the metric performance on collections of segmentation results sourced from carefully compiled two-dimensional synthetic data and three-dimensional medical images. We show that our metrics: (1) penalize errors successfully, especially those around region boundaries; (2) give a low similarity score when existing metrics disagree, thus avoiding overly inflated scores; and (3) score segmentation results over a wider range of values. We analyze a representative metric from this family and the effect of its free parameter on error sensitivity and running time. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)