title: Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI.
abstract: Detection of new Multiple Sclerosis (MS) lesions on magnetic resonance imaging (MRI) is important as a marker of disease activity and as a potential surrogate for relapses. We propose an approach where sequential scans are jointly segmented, to provide a temporally consistent tissue segmentation while remaining sensitive to newly appearing lesions. The method uses a two-stage classification process: 1) a Bayesian classifier provides a probabilistic brain tissue classification at each voxel of reference and follow-up scans, and 2) a random-forest based lesion-level classification provides a final identification of new lesions. Generative models are learned based on 364 scans from 95 subjects from a multi-center clinical trial. The method is evaluated on sequential brain MRI of 160 subjects from a separate multi-center clinical trial, and is compared to 1) semi-automatically generated ground truth segmentations and 2) fully manual identification of new lesions generated independently by nine expert raters on a subset of 60 subjects. For new lesions greater than 0.15 cc in size, the classifier has near perfect performance (99% sensitivity, 2% false detection rate), as compared to ground truth. The proposed method was also shown to exceed the performance of any one of the nine expert manual identifications.
authors.affiliation: Centre for Intelligent Machines, McGill University, Montreal, QC, H3A 0E9 Canada. colm@cim.mcgill.ca
authors.forename: Colm
authors.initials: C
authors.lastname: Elliott
authors.forename: Douglas L
authors.initials: DL
authors.lastname: Arnold
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
authors.forename: Tal
authors.initials: T
authors.lastname: Arbel
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000473:pathology*; D016208:Databases, Factual; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000175:diagnosis / Q000473:pathology; D015203:Reproducibility of Results