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BundleMAP: Anatomically Localized Features from dMRI for Detection of Disease

  • Mohammad Khatami
  • Tobias Schmidt-Wilcke
  • Pia C. Sundgren
  • Amin Abbasloo
  • Bernhard Schölkopf
  • Thomas Schultz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

Abstract

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.

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© Springer International Publishing Switzerland 2015

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.

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Authors and Affiliations

  • Mohammad Khatami
    • 1
  • Tobias Schmidt-Wilcke
    • 2
  • Pia C. Sundgren
    • 3
    • 4
  • Amin Abbasloo
    • 1
  • Bernhard Schölkopf
    • 5
  • Thomas Schultz
    • 1
  1. 1.Department of Computer ScienceUniversity of BonnBonnGermany
  2. 2.Department of NeurologyBergmannsheil University Hospital BochumBochumGermany
  3. 3.Department of Radiology, Department of Clinical SciencesLund UniversityLundSweden
  4. 4.Department of RadiologyUniversity of MichiganAnn ArborUSA
  5. 5.Max Planck Institute for Intelligent SystemsTübingenGermany

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