Bayesian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images

Authors:
Colm Elliott;Simon J. Francis;Douglas L. Arnold;D. Louis Collins;Tal Arbel
Affiliations:
Centre for Intelligent Machines, McGill University, Canada;Montreal Neurological Institute, McGill University, Canada;NeuroRx Research, Montreal, Canada;Montreal Neurological Institute, McGill University, Canada;Centre for Intelligent Machines, McGill University, Canada
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Year:
2010

Citing 2
Cited 0

Spatio-temporal Segmentation of Active Multiple Sclerosis Lesions in Serial MRI Data

IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
STREM: a robust multidimensional parametric method to segment MS lesions in MRI

MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

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Abstract

Accurate and precise identification of multiple sclerosis (MS) lesions in longitudinal MRI is important for monitoring disease progression and for assessing treatment effects. We present a probabilistic framework to automatically detect new, enlarging and resolving lesions in longitudinal scans of MS patients based on multimodal subtraction magnetic resonance (MR) images. Our Bayesian framework overcomes registration artifact by explicitly modeling the variability in the difference images, the tissue transitions, and the neighbourhood classes in the form of likelihoods, and by embedding a classification of a reference scan as a prior. Our method was evaluated on (a) a scan-rescan data set consisting of 3 MS patients and (b) a multicenter clinical data set consisting of 212 scans from 89 RRMS (relapsing-remitting MS) patients. The proposed method is shown to identify MS lesions in longitudinal MRI with a high degree of precision while remaining sensitive to lesion activity.