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)


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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  1. 1.
    Alexander, D.C., Pierpaoli, C., Basser, P.J., Gee, J.C.: Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans. on Medical Imaging 20(11), 1131–1139 (2001)CrossRefGoogle Scholar
  2. 2.
    Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using DT-MRI data. Magnetic Resonance in Medicine 44, 625–632 (2000)CrossRefGoogle Scholar
  3. 3.
    Brun, A., Knutsson, H., Park, H.-J., Shenton, M.E., Westin, C.-F.: Clustering fiber traces using normalized cuts. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 368–375. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  4. 4.
    Colby, J.B., Soderberg, L., Lebel, C., Dinov, I.D., Thompson, P.M., Sowell, E.R.: Along-tract statistics allow for enhanced tractography analysis. NeuroImage 59, 3227–42 (2012)CrossRefGoogle Scholar
  5. 5.
    Corouge, I., Fletcher, P.T., Joshi, S., Gouttard, S., Gerig, G.: Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis. Medical Image Analysis 10, 786–798 (2006)CrossRefGoogle Scholar
  6. 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
  7. 7.
    O’Donnell, L.J., Westin, C.F., Golby, A.J.: Tract-based morphometry for white matter group analysis. NeuroImage 45, 832–844 (2009)CrossRefGoogle Scholar
  8. 8.
    Schmidt-Wilcke, T., Cagnoli, P., Schultz, T., Lotz, A., Mccune, W.J., Sundgren, P.C.: Diminished white matter integrity in patients with systemic lupus erythematosus. NeuroImage: Clinical 5, 291–297 (2014)CrossRefGoogle Scholar
  9. 9.
    Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press (2002)Google Scholar
  10. 10.
    Shalev-Shwartz, S., Shamir, O., Srebro, N., Sridharan, K.: Learnability, stability and uniform convergence. J. of Machine Learning Research 11, 2635–70 (2010)MathSciNetzbMATHGoogle Scholar
  11. 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
  12. 12.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–23 (2000)CrossRefGoogle Scholar
  13. 13.
    Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35, 1459–1472 (2007)CrossRefGoogle Scholar
  14. 14.
    Wakana, S., Caprihan, A., Panzenboeck, M.M., Fallon, J.H., Perry, M., Gollub, R.L., Hua, K., Zhang, J., Jiang, H., Dubey, P., Blitz, A., van Zijl, P., Mori, S.: Reproducibility of quantitative tractography methods applied to cerebral white matter. NeuroImage 36, 630–644 (2007)CrossRefGoogle Scholar
  15. 15.
    Yushkevich, P.A., Zhang, H., Simon, T.J., Gee, J.C.: Structure-specific statistical mapping of white matter tracts. NeuroImage 41, 448–461 (2008)CrossRefGoogle Scholar
  16. 16.
    Zhu, H., Styner, M., Tang, N., Liu, Z., Lin, W., Gilmore, J.H.: FRATS: functional regression analysis of DTI tract statistics. IEEE Trans. on Medical Imaging 29(4), 1039–1049 (2010)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2015

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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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