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. 2017 Jul 15;155:383-393. doi: 10.1016/j.neuroimage.2017.04.018. Epub 2017 Apr 9.
The hippocampus is one of the first brain structures affected by Alzheimer's disease (AD). While many automatic methods for hippocampal segmentation exist, few studies have compared them on the same data. In this study, we compare four fully automated hippocampal segmentation methods in terms of their conformity with manual segmentation and their ability to be used as an AD biomarker in clinical settings. We also apply error correction to the four automatic segmentation methods, and complete a comprehensive validation to investigate differences between the methods. The effect size and classification performance is measured for AD versus normal control (NC) groups and for stable mild cognitive impairment (sMCI) versus progressive mild cognitive impairment (pMCI) groups. Our study shows that the nonlinear patch-based segmentation method with error correction is the most accurate automatic segmentation method and yields the most conformity with manual segmentation (κ=0.894). The largest effect size between AD versus NC and sMCI versus pMCI is produced by FreeSurfer with error correction. We further show that, using only hippocampal volume, age, and sex as features, the area under the receiver operating characteristic curve reaches up to 0.8813 for AD versus NC and 0.6451 for sMCI versus pMCI. However, the automatic segmentation methods are not significantly different in their performance.
海马体是受阿尔茨海默病（AD）影响的第一个大脑结构之一。虽然有许多自动的海马体分割方法，但很少有研究在相同的数据上对它们进行比较。在这项研究中，我们比较了四种完全自动化的海马体分割方法，比较它们与手动分割的一致性，以及在临床环境中作为 AD 生物标志物的能力。我们还对这四种自动分割方法进行了误差校正，并进行了全面的验证，以研究这些方法之间的差异。我们测量了 AD 与正常对照组（NC）之间以及稳定轻度认知障碍（sMCI）与进展性轻度认知障碍（pMCI）之间的效应大小和分类性能。我们的研究表明，具有误差校正的非线性补丁分割方法是最准确的自动分割方法，与手动分割的一致性最高（κ=0.894）。FreeSurfer 具有误差校正，在 AD 与 NC 以及 sMCI 与 pMCI 之间产生的效果最大。我们进一步表明，仅使用海马体体积、年龄和性别作为特征，AD 与 NC 的接收器操作特征曲线下面积高达 0.8813，sMCI 与 pMCI 的面积为 0.6451。然而，自动分割方法的性能没有显著差异。
