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:在本文中,我们描述和验证了纵向磁共振扫描整体脑分割的纵向方法,它以现有的全脑分割方法为基础,可以处理多相数据,并强有力地分析带有白物质损伤的图像。这种方法在此扩展,包括了特定主题的潜在变量,鼓励其分化结果之间的时间一致性,使其能够更好地跟踪数十个神经血管结构的微妙形态变化和白物质损伤。我们验证了拟议的控制对象和患有阿尔茨海默氏病和多发性硬化症的病人多重数据集的方法,并将其结果与最初的跨部门配方和两种基准长距离方法进行比较。结果显示,该方法具有较高的测试可靠性,同时对病人群体之间的纵向疾病影响差异更加敏感。作为开放源神经成形包FreeSurfer的一部分,可以公开使用实施。