Statistical validation metric for accuracy assessment in medical image segmentation
Statistical validation metric for accuracy assessment in medical image segmentation
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DOI：
10.1007/s11548-007-0125-1
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发表时间：
2007-12-01
影响因子：
3
通讯作者：
Radermacher, Klaus
中科院分区：
文献类型：
--
作者：
Popovic, Aleksandra;de la Fuente, Matias;Radermacher, Klaus
目的医学图像分割算法的验证是一个悬而未决的问题，考虑到个体病理的差异和相关的临床对准确性的要求。在本文中，我们提出了一种验证度量，能够区分过度和欠分割，并考虑不同的临床应用。材料和方法在本文中，我们提出了一个验证指标，代表敏感性和特异性之间的权衡。该度量具有区分分割过度或分割不足的优点，这是验证大型分割结果集的重要特征，因为人工检查是累人且耗时的。虽然它以精度测量为导向，但它也与方法的鲁棒性密切相关。结果分析了指标的特征及其医学影响。为了将提出的度量与标准使用的差异度量进行比较，进行了一组数值模拟。该指标通过临床案例研究加以说明，该研究对颅肿瘤分割算法进行了准确性评估，并在6例患者中进行了验证。
Objective Validation of medical image segmentation algorithms is an open question, considering variance of individual pathologies and the related clinical requirements for accuracy. In this paper, we propose a validation metric capable to distinguish between an over and under-segmentation and account for different clinical applications.Materials and methods In this paper, we propose a validation metric representing a tradeoff between sensitivity and specificity. The metric has an advantage of differentiating between an over or under-segmentation which is an important feature for validating large sets of segmentation results, as human inspection is exhausting and time consuming. Although it is oriented to the accuracy measurement it is also closely related to the robustness of a method.Results Features of the metrics are analyzed alongside their medical impact. A set of numerical simulations is performed in order to compare the proposed metric with standardly used discrepancy measures. The metric is illustrated with a clinical case study, presenting accuracy assessment of an algorithm for calvarial tumor segmentation, validated on six patients.