BundleMAP: Anatomically Localized Features from dMRI for Detection of Disease
We present BundleMAP, a novel method for extracting features from diffusion MRI (dMRI), which can be used to detect disease with supervised classification. BundleMAP uses manifold learning to aggregate measurements over localized segments of nerve fiber bundles, which are natural anatomical units in this data. We obtain a fully integrated machine learning pipeline by combining this idea with mechanisms for outlier removal and feature selection. We demonstrate that it increases accuracy on a clinical dataset for which classification results have been reported previously, and that it pinpoints the anatomical locations relevant to the classification.
Unable to display preview. Download preview PDF.
- 6.O’Donnell, L., Schultz, T.: Statistical and machine learning methods for neuroimaging: examples, challenges, and extensions to diffusion imaging data. In: Hotz, I., Schultz, T. (eds.) Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, pp. 293–313. Springer (2015)Google Scholar
- 9.Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press (2002)Google Scholar
- 11.Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E.J.: Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage 31(4), 1487–1505 (2006)CrossRefGoogle Scholar
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.