A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis

Stefano Cerri*, Oula Puonti, Dominik S. Meier, Jens Wuerfel, Mark Mühlau, Hartwig R. Siebner, Koen Van Leemput

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

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.
Original languageEnglish
Article number117471
JournalNeuroImage
Volume225
Number of pages17
ISSN1053-8119
DOIs
Publication statusPublished - 2020

Keywords

  • Lesion segmentation
  • Multiple sclerosis
  • Whole-brain segmentation
  • Generative model

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