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Spatial modeling of multiple sclerosis for disease subtype prediction
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Taschler, Bernd, Ge, Tian, Bendfeldt, Kerstin, Müller-Lenke, Nicole, Johnson, Timothy D. and Nichols, Thomas E. (2014) Spatial modeling of multiple sclerosis for disease subtype prediction. In: Golland, Polina and Hata, Nobuhiko and Barillot, Christian and Haronegger, Joachim and Howe, Robert, (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 : 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part II. Lecture Notes in Computer Science, Volume 8674 . Springer International Publishing, pp. 797-804. ISBN 9783319104690
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Official URL: http://dx.doi.org/10.1007/978-3-319-10470-6_99
Abstract
Magnetic resonance imaging (MRI) has become an essential tool in the diagnosis and managing of Multiple Sclerosis (MS). Currently, the assessment of MS is based on a combination of clinical scores and subjective rating of lesion images by clinicians. In this work we present an objective 5-way classification of MS disease subtype as well as a comparison between three different approaches. First we propose two spatially informed models, a Bayesian Spatial Generalized Linear Mixed Model (BSGLMM) and a Log Gaussian Cox Process (LGCP). The BSGLMM accounts for the binary nature of lesion maps and the spatial dependence between neighboring voxels, and the LGCP accounts for the random spatial variation in lesion location. Both improve upon mass univariate analyses that ignore spatial dependence and rely on some level of arbitrarily defined smoothing of the data. As a comparison, we consider a machine learning approach based on multi-class support vector machine (SVM). For the SVM classification scheme, unlike previous work, we use a large number of quantitative features derived from three MRI sequences in addition to traditional demographic and clinical measures. We show that the spatial models outperform standard approaches with average prediction accuracies of up to 85%.
| Item Type: | Book Item | ||||
|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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| Series Name: | Lecture Notes in Computer Science | ||||
| Publisher: | Springer International Publishing | ||||
| ISBN: | 9783319104690 | ||||
| ISSN: | 0302-9743 | ||||
| Book Title: | Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 : 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part II | ||||
| Editor: | Golland, Polina and Hata, Nobuhiko and Barillot, Christian and Haronegger, Joachim and Howe, Robert | ||||
| Official Date: | 2014 | ||||
| Dates: |
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| Volume: | Volume 8674 | ||||
| Page Range: | pp. 797-804 | ||||
| DOI: | 10.1007/978-3-319-10470-6_99 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published |
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