  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.
