In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test-retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.

翻译：本文描述并验证了一种针对纵向MRI扫描的全脑分割方法。该方法基于现有可处理多对比度数据并能稳健分析存在白质病灶图像的全脑分割技术。我们通过引入个体特异性潜在变量扩展了该方法，这些变量可促进分割结果在时间维度上保持一致性，从而能够更精确地追踪数十个神经解剖学结构与白质病灶的细微形态变化。我们在包含健康对照受试者、阿尔茨海默病及多发性硬化症患者的多组数据集上验证了所提方法，并将其结果与原始横断面方法及两种基准纵向分割方法进行了对比。结果表明，该方法在获得更高重测信度的同时，对患者组间纵向疾病效应差异具有更灵敏的检测能力。该实现已作为开源神经影像工具包FreeSurfer的组成部分公开发布。