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Bayesian Classification of Multiple Sclerosis Lesions in Longitudinal MRI Using Subtraction Images

  • Colm Elliott
  • Simon J. Francis
  • Douglas L. Arnold
  • D. Louis Collins
  • Tal Arbel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6362)

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.

Keywords

Multiple Sclerosis Multiple Sclerosis Patient Multiple Sclerosis Lesion Manually Correct Subtraction Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Colm Elliott
    • 1
  • Simon J. Francis
    • 2
  • Douglas L. Arnold
    • 3
  • D. Louis Collins
    • 2
  • Tal Arbel
    • 1
  1. 1.Centre for Intelligent MachinesMcGill UniversityCanada
  2. 2.Montreal Neurological InstituteMcGill UniversityCanada
  3. 3.NeuroRx ResearchMontrealCanada

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