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)


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.


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.


  1. 1.
    Lee, M.A., Smith, S., et al.: Defining multiple sclerosis disease activity using MRI T2-weighted difference imaging. Brain 121, 2095–2102 (1998)CrossRefGoogle Scholar
  2. 2.
    Tan, I.L., van Schijndel, R.A., et al.: Image Registration and subtraction to detect active T2 lesions in MS: an interobserver study. J. Neurol. 249, 767–773 (2002)CrossRefGoogle Scholar
  3. 3.
    Moraal, B., Meier, D.S., et al.: Subtraction MR Images in a Multiple Sclerosis Multicenter Clinical Trial Setting. Radiology 250, 506–514 (2009)CrossRefGoogle Scholar
  4. 4.
    Duan, Y., Hildenbrand, P.G., et al.: Segmentation of Subtraction Images for the Measurement of Lesion Change in Multiple Sclerosis. Am. J. Neuroradiol. 29, 340–346 (2008)CrossRefGoogle Scholar
  5. 5.
    Rey, D., Subsol, G., et al.: Automatic detection and segmentation of evolving processes in 3D medical images: Application to multiple sclerosis. Med. Image Anal. 6, 163–179 (2002)CrossRefGoogle Scholar
  6. 6.
    Welti, D., Gerig, G., et al.: Spatio-temporal Segmentation of Active Multiple Sclerosis Lesions in Serial MRI Data. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, p. 438. Springer, Heidelberg (2001)Google Scholar
  7. 7.
    Prima, S., Arnold, D.L., et al.: Multivariate Statistics for Detection of MS Activity in Serial Multimodal MR Images. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 663–670. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Aït-Ali, L.S., Prima, S., et al.: STREM: A Robust Multidimensional Parametric Method to Segment MS Lesions in MRI. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 409–416. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Bosc, M., Heitz, F., et al.: Automatic change detection in mutimodal serial MRI: application to multiple sclerosis lesion evolution. NeuroImage 20, 643–656 (2003)CrossRefGoogle Scholar
  10. 10.
    Thirion, J.-P., Calmon, G.: Deformation Analysis to Detect and Quantify Active Lesions in Three-Dimensional Medical Image Sequences. TMI 18, 429–441 (1999)Google Scholar
  11. 11.
    Turlach, B.: Bandwidth selection in kernel density estimation: a review. Discussion paper 9317, Institut de Statistique, UCL, Louvain la Neuve, Belgium (1993)Google Scholar
  12. 12.
    Sled, J.G., Zijdenbos, et. al.: A non-parametric method for automatic correction of intensity nonuniformity in MRI data. TMI 17, 87–97 (1998)Google Scholar
  13. 13.
    Nyùl, L.G., Udupa, J.K., et al.: New variants of a method of MRI scale standardization. TMI 19, 143–150 (2000)Google Scholar
  14. 14.
    Francis, S.: Automatic lesion identification in MRI of MS patients. Master’s Thesis, McGill University (2004)Google Scholar
  15. 15.
    Meier, D.S., Guttman, R.G.: Time-series analysis of MRI intensity patterns in multiple sclerosis. NeuroImage 20, 1193–1209 (2003)CrossRefGoogle Scholar

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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