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.
This work was supported by NSERC Strategic Grant (350547-07).
Chapter PDF
Similar content being viewed by others
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.
References
Lee, M.A., Smith, S., et al.: Defining multiple sclerosis disease activity using MRI T2-weighted difference imaging. Brain 121, 2095–2102 (1998)
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)
Moraal, B., Meier, D.S., et al.: Subtraction MR Images in a Multiple Sclerosis Multicenter Clinical Trial Setting. Radiology 250, 506–514 (2009)
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)
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)
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)
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)
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)
Bosc, M., Heitz, F., et al.: Automatic change detection in mutimodal serial MRI: application to multiple sclerosis lesion evolution. NeuroImage 20, 643–656 (2003)
Thirion, J.-P., Calmon, G.: Deformation Analysis to Detect and Quantify Active Lesions in Three-Dimensional Medical Image Sequences. TMI 18, 429–441 (1999)
Turlach, B.: Bandwidth selection in kernel density estimation: a review. Discussion paper 9317, Institut de Statistique, UCL, Louvain la Neuve, Belgium (1993)
Sled, J.G., Zijdenbos, et. al.: A non-parametric method for automatic correction of intensity nonuniformity in MRI data. TMI 17, 87–97 (1998)
Nyùl, L.G., Udupa, J.K., et al.: New variants of a method of MRI scale standardization. TMI 19, 143–150 (2000)
Francis, S.: Automatic lesion identification in MRI of MS patients. Master’s Thesis, McGill University (2004)
Meier, D.S., Guttman, R.G.: Time-series analysis of MRI intensity patterns in multiple sclerosis. NeuroImage 20, 1193–1209 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Elliott, C., Francis, S.J., Arnold, D.L., Collins, D.L., Arbel, T. (2010). Bayesian Classification of Multiple Sclerosis Lesions in Longitudinal MRI Using Subtraction Images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15745-5_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-15745-5_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15744-8
Online ISBN: 978-3-642-15745-5
eBook Packages: Computer ScienceComputer Science (R0)