A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis

• Published in: NeuroImage (Orlando, Fla.) Vol. 225; p. 117471
• Main Authors: Cerri, Stefano, Puonti, Oula, Meier, Dominik S., Wuerfel, Jens, Mühlau, Mark, Siebner, Hartwig R., Van Leemput, Koen
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.01.2021; Elsevier Limited; Elsevier
• Subjects: Algorithms; Anatomy; Atrophy; Atrophy - pathology; Automation; Brain - diagnostic imaging; Brain - pathology; Brain architecture; Generative model; Gray Matter - pathology; Human performance; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Lesion segmentation; Lesions; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Methods; Multiple sclerosis; Multiple Sclerosis - diagnostic imaging; Multiple Sclerosis - pathology; Neural networks; Neuroimaging; Open source software; Segmentation; Substantia alba; Substantia grisea; White Matter - pathology; Whole-brain segmentation; Multiple sclerosis; Lesion segmentation; Whole-brain segmentation; Generative model
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.117471

•A method for segmenting white matter lesions and dozens of brain structures in MS.•The method is adaptive to different scanners and MRI sequences.•It can be used to quantify brain volumes without resorting to lesion-filling.•The method is publicly available as part of FreeSurfer. 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.