  Here we present a method for the simultaneous segmentation of white matter
lesions and normal-appearing neuroanatomical structures from multi-contrast
brain MRI scans of multiple sclerosis patients. The method integrates a novel
model for white matter lesions into a previously validated generative model for
whole-brain segmentation. By using separate models for the shape of anatomical
structures and their appearance in MRI, the algorithm can adapt to data
acquired with different scanners and imaging protocols without retraining. We
validate the method using four disparate datasets, showing robust performance
in white matter lesion segmentation while simultaneously segmenting dozens of
other brain structures. We further demonstrate that the contrast-adaptive
method can also be safely applied to MRI scans of healthy controls, and
replicate previously documented atrophy patterns in deep gray matter structures
in MS. The algorithm is publicly available as part of the open-source
neuroimaging package FreeSurfer.
