Deep Spectral-Based Shape Features for Alzheimer’s Disease Classification

  • Mahsa Shakeri
  • Herve Lombaert
  • Shashank Tripathi
  • Samuel Kadoury
  • for the Alzheimer’s Disease Neuroimaging Initiative
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10126)


Alzheimer’s disease (AD) and mild cognitive impairment (MCI) are the most prevalent neurodegenerative brain diseases in elderly population. Recent studies on medical imaging and biological data have shown morphological alterations of subcortical structures in patients with these pathologies. In this work, we take advantage of these structural deformations for classification purposes. First, triangulated surface meshes are extracted from segmented hippocampus structures in MRI and point-to-point correspondences are established among population of surfaces using a spectral matching method. Then, a deep learning variational auto-encoder is applied on the vertex coordinates of the mesh models to learn the low dimensional feature representation. A multi-layer perceptrons using softmax activation is trained simultaneously to classify Alzheimer’s patients from normal subjects. Experiments on ADNI dataset demonstrate the potential of the proposed method in classification of normal individuals from early MCI (EMCI), late MCI (LMCI), and AD subjects with classification rates outperforming standard SVM based approach.


Classification Spectral matching Variational autoencoder Alzheimer’s disease 



Funding was provided by the Canada Research Chairs and from the CHU Sainte-Justine Hospital’s Research Center, Montreal, Canada.

ADNI data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics.

The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.


  1. 1.
    Ranginwala, N.A., Hynan, L.S., Weiner, M.F., White, C.L.I.: Clinical criteria for the diagnosis of Alzheimer disease: still good after all these years. Am. J. Geriatr. Psychiatry 16(5), 384–388 (2008)CrossRefGoogle Scholar
  2. 2.
    Petersen, R., Smith, G., Waring, S., Ivnik, R., Tangalos, E., Kokmen, E.: Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56(3), 303–308 (1999)CrossRefGoogle Scholar
  3. 3.
    Du, A.T., Schuff, N., et al.: Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 71, 441–447 (2001)CrossRefGoogle Scholar
  4. 4.
    Wyman, B., Harvey, D., Crawford, K., Bernstein, M., Carmichael, O., Cole, P., Crane, P., Decarli, C., Fox, N., Gunter, J., Hill, D., Killiany, R., Pachai, C., Schwarz, A., Schuff, N., Senjem, M., Suhy, J., Thompson, P., Weiner, M., Jack, C.: Standardization of analysis sets for reporting results from ADNI MRI data. Alzheimer’s Dement. 9(3), 332–337 (2013)CrossRefGoogle Scholar
  5. 5.
    Davatzikos, C., Fan, Y., Wu, X., Shen, D., Resnick, S.: Alzheimer’s disease via pattern classification of MRI. Neurobiol. Aging 29(4), 514–523 (2008)CrossRefGoogle Scholar
  6. 6.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: International Conference on Learning Representations (ICLR) (2013)Google Scholar
  8. 8.
    Lombaert, H., Criminisi, A., Ayache, N.: Spectral forests: learning of surface data, application to cortical parcellation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 547–555. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24553-9_67 CrossRefGoogle Scholar
  9. 9.
    Lombaert, H., Grady, L., Polimeni, J.R., Cheriet, F.: FOCUSR: feature oriented correspondence using spectral regularization – a method for precise surface matching. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2143–2160 (2013)CrossRefGoogle Scholar
  10. 10.
    Lombaert, H., Sporring, J., Siddiqi, K.: Diffeomorphic spectral matching of cortical surfaces. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 376–389. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38868-2_32 CrossRefGoogle Scholar
  11. 11.
    Wachinger, C., Reuter, M.: Domain adaptation for Alzheimer’s disease diagnostics. NeuroImage 139, 470–479 (2016)CrossRefGoogle Scholar
  12. 12.
    Suk, H.-I., Shen, D.: Deep learning-based feature representation for AD/MCI classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 583–590. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40763-5_72 CrossRefGoogle Scholar
  13. 13.
    Grady, L.J., Polimeni, J.R.: Discrete Calculus. Springer, Heidelberg (2010)CrossRefMATHGoogle Scholar
  14. 14.
    Shakeri, M., Lombaert, H., Datta, A.N., Oser, N., Ltourneau-Guillon, L., Lapointe, L.V., Martin, F., Malfait, D., Tucholka, A., Lippe, S., Kadoury, S.: Statistical shape analysis of subcortical structures using spectral matching. Comput. Med. Imaging Graph. 52, 58–71 (2016)CrossRefGoogle Scholar
  15. 15.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Inc., New York (1995)MATHGoogle Scholar
  16. 16.
    Jack, C., Bernstein, M., Fox, N., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging: JMRI 27(4), 685–691 (2008)CrossRefGoogle Scholar
  17. 17.
    Patenaude, B., Smith, S.M., Kennedy, D.N., Jenkinson, M.: A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage 56, 907–922 (2011)CrossRefGoogle Scholar
  18. 18.
    Goryawala, M., et al.: Inclusion of neuropsychological scores in atrophy models improves diagnostic classification of Alzheimer’s disease and mild cognitive impairment. Comput. Intell. Neurosci. 2015, 56 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Mahsa Shakeri
    • 1
    • 2
  • Herve Lombaert
    • 3
  • Shashank Tripathi
    • 1
  • Samuel Kadoury
    • 1
    • 2
  • for the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.MedicalPolytechnique MontrealMontrealCanada
  2. 2.CHU Sainte-Justine Research CenterMontrealCanada
  3. 3.Inria Sophia-AntipolisValbonneFrance

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