Procrustes-based geometric morphometrics on MRI images: An example of inter-operator bias in 3D landmarks and its impact on big datasets.
Procrustes-based geometric morphometrics on MRI images: An example of inter-operator bias in 3D landmarks and its impact on big datasets.
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DOI：
10.1371/journal.pone.0197675
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Cardini A
中科院分区：
文献类型：
--
作者：
Daboul A;Ivanovska T;Bülow R;Biffar R;Cardini A
使用成人头部 MRI 的 3D 解剖标志，我们评估了基于 Procrustes 的几何形态分析中操作者间差异的程度。由三个不同的操作员对个体的子样本进行了复制数字化，对绝对误差和相对误差进行了深入分析。还在 900 多人的大样本中探讨了操作者间差异的影响。尽管绝对误差对于 MRI 测量（包括骨标志）并不罕见，但形状尤其受到操作员之间差异的影响，高达 30% 以上的样本变异是由此类误差造成的。偏差的程度如此之大，以至于它主导了骨骼和总体（包括所有地标）形状变化的主要模式，很大程度上超过了数百名男性和女性之间性别差异的影响。然而，相比之下，尽管鼻腔大小估计的误差相对较大，但软组织鼻标志的再现性较高。我们的研究举例说明了使用几何形态计量学对 MRI 标志物进行测量误差的评估，并强调了将其与所使用的特定方法框架内的总样本方差相关联的重要性。总之，精确的地标不一定意味着可忽略的误差，尤其是在形状数据中；事实上，尺寸和形状可能会受到测量误差的不同影响，并且不同类型的地标可能具有相对较大或较小的误差。重要的是，与最近在数字图像上使用几何形态测量学的其他研究（然而，这些研究并非特定于 MRI 数据）一致，这项研究表明，操作者间偏差可能是大样本分析中误差的主要来源，因为这些偏差在“大数据时代”变得越来越普遍。
Using 3D anatomical landmarks from adult human head MRIs, we assessed the magnitude of inter-operator differences in Procrustes-based geometric morphometric analyses. An in depth analysis of both absolute and relative error was performed in a subsample of individuals with replicated digitization by three different operators. The effect of inter-operator differences was also explored in a large sample of more than 900 individuals. Although absolute error was not unusual for MRI measurements, including bone landmarks, shape was particularly affected by differences among operators, with up to more than 30% of sample variation accounted for by this type of error. The magnitude of the bias was such that it dominated the main pattern of bone and total (all landmarks included) shape variation, largely surpassing the effect of sex differences between hundreds of men and women. In contrast, however, we found higher reproducibility in soft-tissue nasal landmarks, despite relatively larger errors in estimates of nasal size. Our study exemplifies the assessment of measurement error using geometric morphometrics on landmarks from MRIs and stresses the importance of relating it to total sample variance within the specific methodological framework being used. In summary, precise landmarks may not necessarily imply negligible errors, especially in shape data; indeed, size and shape may be differentially impacted by measurement error and different types of landmarks may have relatively larger or smaller errors. Importantly, and consistently with other recent studies using geometric morphometrics on digital images (which, however, were not specific to MRI data), this study showed that inter-operator biases can be a major source of error in the analysis of large samples, as those that are becoming increasingly common in the 'era of big data'.
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