Characterizing Pathological Deviations from Normality Using Constrained Manifold-Learning

  • Nicolas Duchateau
  • Mathieu De Craene
  • Gemma Piella
  • Alejandro F. Frangi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)


We propose a technique to represent a pathological pattern as a deviation from normality along a manifold structure. Each subject is represented by a map of local motion abnormalities, obtained from a statistical atlas of motion built from a healthy population. The algorithm learns a manifold from a set of patients with varying degrees of the same pathology. The approach extends recent manifold-learning techniques by constraining the manifold to pass by a physiologically meaningful origin representing a normal motion pattern. Individuals are compared to the manifold population through a distance that combines a mapping to the manifold and the path along the manifold to reach its origin. The method is applied in the context of cardiac resynchronization therapy (CRT), focusing on a specific motion pattern of intra-ventricular dyssynchrony called septal flash (SF). We estimate the manifold from 50 CRT candidates with SF and test it on 38 CRT candidates and 21 healthy volunteers. Experiments highlight the need of nonlinear techniques to learn the studied data, and the relevance of the computed distance for comparing individuals to a specific pathological pattern.


Cardiac Resynchronization Therapy Reconstruction Error Mechanical Dyssynchrony Kernel Principal Component Analysis Manifold Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Davis, B.C., Fletcher, P.T., Bullitt, E., Joshi, S.: Population shape regression from random design data. Int. J. Comput. Vis. 90, 255–266 (2010)CrossRefGoogle Scholar
  2. 2.
    Duchateau, N., De Craene, M., Piella, G., Silva, E., Doltra, A., Sitges, M., Bijnens, B.H., Frangi, A.F.: A spatiotemporal statistical atlas of motion for the quantification of abnormal myocardial tissue velocities. Med. Image Anal. 15(3), 316–328 (2011)CrossRefGoogle Scholar
  3. 3.
    Etyngier, P., Keriven, R., Segonne, F.: Projection onto a shape manifold for image segmentation with prior. In: 14th IEEE International Conference on Image Processing, pp. IV361–IV364. IEEE Press, New York (2007)Google Scholar
  4. 4.
    Fletcher, P.T., Lu, C., Pizer, S.M., Joshi, S.: Principal geodesic analysis for the study of nonlinear statistics of shape. IEEE Trans. Med. Imaging 23(8), 995–1005 (2004)CrossRefGoogle Scholar
  5. 5.
    Fornwalt, B.K.: The dyssynchrony in predicting response to cardiac resynchronization therapy: A call for change. J. Am. Soc. Echocardiogr. 24(2), 180–184 (2011)CrossRefGoogle Scholar
  6. 6.
    Gerber, S., Tasdizen, T., Fletcher, P.T., Joshi, S., Whitaker, R.: Manifold modeling for brain population analysis. Med. Image Anal. 14(5), 643–653 (2010)CrossRefGoogle Scholar
  7. 7.
    Kim, K.-H., Choi, S.: Neighbor search with global geometry: a minimax message passing algorithm. In: 24th International Conference on Machine Learning, pp. 401–408 (2007)Google Scholar
  8. 8.
    Kwok, J.T.-Y., Tsang, I.W.-H.: The pre-image problem in kernel methods. IEEE Trans. Neural Netw. 15(6), 1517–1525 (2004)CrossRefGoogle Scholar
  9. 9.
    Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)CrossRefGoogle Scholar
  10. 10.
    Mika, S., Schölkopf, B., Smola, A., Müller, K.R., Scholz, M., Rätsch, G.: Kernel PCA and de-noising in feature spaces. Adv. Neural Inf. Process Syst. 11, 536–542 (1999)Google Scholar
  11. 11.
    Parsai, C., Bijnens, B.H., Sutherland, G.R., Baltabaeva, A., Claus, P., Marciniak, M., Paul, V., Scheffer, M., Donal, E., Derumeaux, G., Anderson, L.: Toward understanding response to cardiac resynchronization therapy: left ventricular dyssynchrony is only one of multiple mechanisms. Eur. Heart J. 30(8), 940–949 (2009)CrossRefGoogle Scholar
  12. 12.
    Saitoh, S.: Theory of Reproducing Kernels and its Applications. Pitman Res. Notes Math. Ser., p. 189. Wiley, Chichester (1988)zbMATHGoogle Scholar
  13. 13.
    Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  14. 14.
    Worsley, K.J., Taylor, J.E., Tomaiuolo, F., Lerch, J.: Unified univariate and multivariate random field theory. NeuroImage 23(suppl. 1), S189–S195 (2004)CrossRefGoogle Scholar
  15. 15.
    Zheng, W.S., Lai, J.-H., Yuen, P.C.: Penalized preimage learning in kernel principal component analysis. IEEE Trans. Neural Netw. 21(4), 551–570 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nicolas Duchateau
    • 1
  • Mathieu De Craene
    • 1
  • Gemma Piella
    • 1
  • Alejandro F. Frangi
    • 1
  1. 1.Center for Computational Imaging & Simulation Technologies in Biomedicine(CISTIB) – Universitat Pompeu Fabra and CIBER-BBNBarcelonaSpain

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