McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada; Department of Biomedical Engineering, McGill University, Montreal, Canada.
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
Neuroimage. 2018 Nov 1;181:142-148. doi: 10.1016/j.neuroimage.2018.06.077. Epub 2018 Jun 30.
Recently, a group of major international experts have completed a comprehensive effort to efficiently define a harmonized protocol for manual hippocampal segmentation that is optimized for Alzheimer's research (known as the EADC-ADNI Harmonized Protocol (the HarP)). This study compares the HarP with one of the widely used hippocampal segmentation protocols (Pruessner, 2000), based on a single automatic segmentation method trained separately with libraries made from each manual segmentation protocol. The automatic segmentation conformity with the corresponding manual segmentation and the ability to capture Alzheimer's disease related hippocampal atrophy on large datasets are measured to compare the manual protocols. In addition to the possibility of harmonizing different procedures of hippocampal segmentation, our results show that using the HarP, the automatic segmentation conformity with manual segmentation is also preserved (Dice's κ=0.88,κ=0.87 for Pruessner and HarP respectively (p = 0.726 for common training library)). Furthermore, the results show that the HarP can capture the Alzheimer's disease related hippocampal volume differences in large datasets. The HarP-derived segmentation shows large effect size (Cohen's d = 1.5883) in separating Alzheimer's Disease patients versus normal controls (AD:NC) and medium effect size (Cohen's d = 0.5747) in separating stable versus progressive Mild Cognitively Impaired patients (sMCI:pMCI). Furthermore, the area under the ROC curve for a LDA classifier trained based on age, sex and HarP-derived hippocampal volume is 0.8858 for AD:NC, and for 0.6677 sMCI:pMCI. These results show that the harmonized protocol-derived labels can be widely used in clinic and research, as a sensitive and accurate way of delineating the hippocampus.
最近,一组国际专家完成了一项全面的工作,旨在为阿尔茨海默病研究(即 EADC-ADNI 协调协议(HarP))高效定义一个优化的手动海马体分割协调协议。本研究比较了 HarP 与广泛使用的海马体分割协议之一(Pruessner,2000 年),该协议基于分别使用每个手动分割协议库训练的单一自动分割方法。通过比较手动协议,来衡量自动分割与相应手动分割的一致性以及在大型数据集上捕获与阿尔茨海默病相关的海马体萎缩的能力。除了协调不同的海马体分割程序的可能性之外,我们的结果还表明,使用 HarP,自动分割与手动分割的一致性也得以保留(Dice 的 κ=0.88,Pruessner 和 HarP 分别为 0.87(p=0.726 用于公共训练库))。此外,结果表明 HarP 可以在大型数据集中捕获与阿尔茨海默病相关的海马体体积差异。HarP 分割得到的结果在区分阿尔茨海默病患者与正常对照组(AD:NC)时具有较大的效应量(Cohen's d=1.5883),在区分稳定与进展性轻度认知障碍患者(sMCI:pMCI)时具有中等的效应量(Cohen's d=0.5747)。此外,基于年龄、性别和 HarP 分割得到的海马体体积训练的 LDA 分类器的 ROC 曲线下面积为 AD:NC 为 0.8858,sMCI:pMCI 为 0.6677。这些结果表明,协调协议得到的标签可以在临床和研究中广泛使用,作为一种敏感和准确的描绘海马体的方法。