A nonparametric procedure for comparing the areas under correlated LROC curves.
A nonparametric procedure for comparing the areas under correlated LROC curves.
复制标题
用于比较相关 LROC 曲线下面积的非参数程序。
DOI：
10.1109/tmi.2012.2205015
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发表时间：
2012
影响因子：
10.6
通讯作者：
Noo,Frédéric
中科院分区：
文献类型：
--
作者：
Wunderlich,Adam;Noo,Frédéric
与接受者工作特征（ROC）评估范式相比，定位ROC （LROC）分析提供了一种联合评估观察者研究中定位和检测准确性的方法。在典型的多读卡器多病例（MRMC）评估中，数据集是配对的，以便在读卡器之间和在成像条件（例如，重建方法或扫描参数）之间比较观察者的性能产生相关性。因此，MRMC评估激发了对比较相关LROC曲线的统计方法的需求。在本文中，我们提出了一种非参数策略。具体来说，我们发现Sen关于u统计量的开创性工作可以应用于估计LROC面积估计向量的协方差矩阵。得到的协方差估计量是DeLong给出的用于ROC分析的协方差估计量的LROC模拟。一旦协方差矩阵被估计，它可以用来构建置信区间和/或置信区域，以比较不同成像条件下观察者的性能。此外，考虑到小规模试点研究的结果，协方差估计器可用于估计在全面观察者研究中达到所需置信区间大小所需的图像和观察者的数量。我们的方法的效用是通过人类观察者LROC评估扇形束x射线计算机断层扫描的三种图像重建策略来说明的。
In contrast to the receiver operating characteristic (ROC) assessment paradigm, localization ROC (LROC) analysis provides a means to jointly assess the accuracy of localization and detection in an observer study. In a typical multireader, multicase (MRMC) evaluation, the data sets are paired so that correlations arise in observer performance both between readers and across the imaging conditions (e.g., reconstruction methods or scanning parameters) being compared. Therefore, MRMC evaluations motivate the need for a statistical methodology to compare correlated LROC curves. In this paper, we suggest a nonparametric strategy for this purpose. Specifically, we find that seminal work of Sen on U-statistics can be applied to estimate the covariance matrix for a vector of LROC area estimates. The resulting covariance estimator is the LROC analog of the covariance estimator given by DeLong for ROC analysis. Once the covariance matrix is estimated, it can be used to construct confidence intervals and/or confidence regions for purposes of comparing observer performance across imaging conditions. In addition, given the results of a small-scale pilot study, the covariance estimator may be used to estimate the number of images and observers needed to achieve a desired confidence interval size in a full-scale observer study. The utility of our methodology is illustrated with a human-observer LROC evaluation of three image reconstruction strategies for fan-beam X-ray computed tomography.