Asman Andrew J, Scoggins Andrew G, Prince Jerry L, Landman Bennett A
Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235.
Proc SPIE Int Soc Opt Eng. 2011;7962:79623G. doi: 10.1117/12.877471.
Labeling or parcellation of structures of interest on magnetic resonance imaging (MRI) is essential in quantifying and characterizing correlation with numerous clinically relevant conditions. The use of statistical methods using automated methods or complete data sets from several different raters have been proposed to simultaneously estimate both rater reliability and true labels. An extension to these statistical based methodologies was proposed that allowed for missing labels, repeated labels and training trials. Herein, we present and demonstrate the viability of these statistical based methodologies using real world data contributed by minimally trained human raters. The consistency of the statistical estimates, the accuracy compared to the individual observations and the variability of both the estimates and the individual observations with respect to the number of labels are discussed. It is demonstrated that the Gaussian based statistical approach using the previously presented extensions successfully performs label fusion in a variety of contexts using data from online (Internet-based) collaborations among minimally trained raters. This first successful demonstration of a statistically based approach using "wild-type" data opens numerous possibilities for very large scale efforts in collaboration. Extension and generalization of these technologies for new application spaces will certainly present fascinating areas for continuing research.
在磁共振成像（MRI）上对感兴趣的结构进行标记或分割，对于量化和表征与众多临床相关病症的相关性至关重要。有人提出使用自动化方法或来自多个不同评分者的完整数据集的统计方法，来同时估计评分者的可靠性和真实标签。对这些基于统计的方法进行了扩展，使其能够处理缺失标签、重复标签和训练试验。在此，我们展示并论证了使用训练不足的人类评分者提供的真实世界数据的这些基于统计的方法的可行性。讨论了统计估计的一致性、与个体观察结果相比的准确性以及估计值和个体观察结果相对于标签数量的变异性。结果表明，使用先前提出的扩展的基于高斯的统计方法，利用来自训练不足的评分者在线（基于互联网）合作的数据，在各种情况下都能成功地进行标签融合。这种首次使用“野生型”数据成功展示的基于统计的方法，为大规模合作努力开辟了众多可能性。将这些技术扩展和推广到新的应用领域，无疑将为持续研究带来迷人的领域。
