Abstract
The objective of this paper is to classify Multiple Sclerosis courses using features extracted from Magnetic Resonance Spectroscopic Imaging (MRSI) combined with brain tissue segmentations of gray matter, white matter, and lesions. To this purpose we trained several classifiers, ranging from simple (i.e. Linear Discriminant Analysis) to state-of-the-art (i.e. Convolutional Neural Networks). We investigate four binary classification tasks and report maximum values of Area Under receiver operating characteristic Curve between 68% and 95%. Our best results were found after training Support Vector Machines with gaussian kernel on MRSI features combined with brain tissue segmentation features.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Compston, A., Coles, A.: Multiple sclerosis. Lancet 372(9648), 1502–1518 (2008)
Miller, D.H., Chard, D.T., Ciccarelli, O.: Clinically isolated syndromes. Lancet Neurolog. 11(2), 157–169 (2012)
Scalfari, A., Neuhaus, A., Degenhardt, A., Rice, G.P., Muraro, P.A., Daumer, M., Ebers, G.C.: The natural history of multiple sclerosis, a geographically based study 10: relapses and long-term disability. Brain 133(7), 1914–1929 (2010)
McDonald, W.I., Compston, A., Edan, G., Goodkin, D., Hartung, H.P., Lublin, F.D., McFarland, H.F., Paty, D.W., Polman, C.H., Reingold, S.C., et al.: Recommended diagnostic criteria for multiple sclerosis: guidelines from the international panel on the diagnosis of multiple sclerosis. Ann. Neurolog. 50(1), 121–127 (2001)
Polman, C.H., Reingold, S.C., Edan, G., Filippi, M., Hartung, H.P., Kappos, L., Lublin, F.D., Metz, L.M., McFarland, H.F., O’Connor, P.W., et al.: Diagnostic criteria for multiple sclerosis: 2005 revisions to the McDonald criteria. Ann. Neurolog. 58(6), 840–846 (2005)
Polman, C.H., Reingold, S.C., Banwell, B., Clanet, M., Cohen, J.A., Filippi, M., Fujihara, K., Havrdova, E., Hutchinson, M., Kappos, L., et al.: Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann. Neurolog. 69(2), 292–302 (2011)
Rovira, À., Auger, C., Alonso, J.: Magnetic resonance monitoring of lesion evolution in multiple sclerosis. Ther. Adv. Neurolog. Disord. 6(5), 298–310 (2013)
Lublin, F.D., Reingold, S.C., et al.: Defining the clinical course of multiple sclerosis results of an international survey. Neurology 46(4), 907–911 (1996)
Jain, S., Sima, D.M., Ribbens, A., Cambron, M., Maertens, A., Van Hecke, W., De Mey, J., Barkhof, F., Steenwijk, M.D., Daams, M., et al.: Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. NeuroImage Clin. 8, 367–375 (2015)
Poullet, J.B.: Quantification and classification of magnetic resonance spectroscopic data for brain tumor diagnosis. Katholic University of Leuven (2008)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2), 179–188 (1936)
Breiman, L.: Random Forests. Mach. Learn. 45(1), 5–32 (2001)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint (2014). arXiv:1409.1556
Chollet, F.: Keras (2015). https://github.com/fchollet/keras
Theano Development Team: Theano: a Python framework for fast computation of mathematical expressions. arXiv e-prints abs/1605.02688. http://arxiv.org/abs/1605.02688
Muthuraman, M., Fleischer, V., Kolber, P., Luessi, F., Zipp, F., Groppa, S.: Structural brain network characteristics can differentiate CIS from early RRMS. Front. Neurosci. 10 (2016). Article no. 14
Kocevar, G., Stamile, C., Hannoun, S., Cotton, F., Vukusic, S., Durand-Dubief, F., Sappey-Marinier, D.: Graph theory-based brain connectivity for automatic classification of multiple sclerosis clinical courses. Front. Neurosci. 10, 478 (2016)
Acknowledgments.
This work was funded by European project EU MC ITN TRANSACT 2012 (No. 316679) and the ERC Advanced Grant BIOTENSORS nr.339804. EU: The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013). This paper reflects only the authors’ views and the Union is not liable for any use that may be made of the contained information.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ion-Mărgineanu, A. et al. (2017). A Comparison of Machine Learning Approaches for Classifying Multiple Sclerosis Courses Using MRSI and Brain Segmentations. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_73
Download citation
DOI: https://doi.org/10.1007/978-3-319-68612-7_73
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68611-0
Online ISBN: 978-3-319-68612-7
eBook Packages: Computer ScienceComputer Science (R0)