Longitudinal Scoliotic Trunk Analysis via Spectral Representation and Statistical Analysis

  • Ola Ahmad
  • Herve Lombaert
  • Stefan Parent
  • Hubert Labelle
  • Jean Dansereau
  • Farida Cheriet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10126)

Abstract

Scoliosis is a complex 3D deformation of the spine leading to asymmetry of the external shape of the human trunk. A clinical follow-up of this deformation is decisive for its treatment, which depends on the spinal curvature but also on the deformity’s progression over time. This paper presents a new method for longitudinal analysis of scoliotic trunks based on spectral representation of shapes combined with statistical analysis. The spectrum of the surface model is used to compute the correspondence between deformable scoliotic trunks. Spectral correspondence is combined with Canonical Correlation Analysis to do point-wise feature comparison between models. This novel combination allows us to efficiently capture within-subject shape changes to assess scoliosis progression (SP). We tested our method on 23 scoliotic patients with right thoracic curvature. Quantitative comparison with spinal measurements confirms that our method is able to identify significant changes associated with SP.

References

  1. 1.
    Adankon, M.M., Chihab, N., Dansereau, J., Labelle, H., Cheriet, F.: Scoliosis follow-up using noninvasive trunk surface acquisition. IEEE Trans. Biomed. Eng. 60(8), 2262–2270 (2013)CrossRefGoogle Scholar
  2. 2.
    Ahmad, O., Collet, C.: Scale-space spatio-temporal random fields: application to the detection of growing microbial patterns from surface roughness. Pattern Recogn. 58, 27–38 (2016)CrossRefGoogle Scholar
  3. 3.
    Ajemba, P.O., Durdle, N.G., Raso, V.J.: Characterizing torso shape deformity in scoliosis using structured splines models. IEEE Trans. Biomed. Eng. 56(6), 1652–1662 (2009)CrossRefGoogle Scholar
  4. 4.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)CrossRefMATHGoogle Scholar
  5. 5.
    Buchanan, R., Birch, J.G., Morton, A.A., Browne, R.H.: Do you see what I see? Looking at scoliosis surgical outcomes through orthopedists’ eyes. Spine 28(24), 2700–2704 (2003). discussion 2705CrossRefGoogle Scholar
  6. 6.
    Carr, J.C., Beatson, R.K., Cherrie, J.B., Mitchell, T.J., Fright, W.R., McCallum, B.C., Evans, T.R.: Reconstruction and representation of 3D objects with radial basis functions. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2001, pp. 67–76. ACM, New York (2001)Google Scholar
  7. 7.
    Cobb, J.R.: Outline for the study of scoliosis. Am. Acad. Orthop. Surg. Instruct. Lect. 5, 261–275 (1984)Google Scholar
  8. 8.
    Fischl, B., Sereno, M.I., Tootell, R.B., Dale, A.M.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8(4), 272–284 (1999)CrossRefGoogle Scholar
  9. 9.
    Grigis, A., Noblet, V., Heitz, F., Blanc, F., de Sèze, J., Kremer, S., Rumbach, L., Armspach, J.P.: Longitudinal change detection in diffusion MRI using multivariate statistical testing on tensors. NeuroImage 60(4), 2206–2221 (2012)CrossRefGoogle Scholar
  10. 10.
    Hackenberg, L., Hierholzer, E., Pötzl, W., Götze, C., Liljenqvist, U.: Rasterstereographic back shape analysis in idiopathic scoliosis after posterior correction and fusion. Clin. Biomech. 18(10), 883–889 (2003)CrossRefGoogle Scholar
  11. 11.
    Hotelling, H.: Relations between two sets of variates. Biometrika XXVIII, 321–377 (1936)CrossRefMATHGoogle Scholar
  12. 12.
    Jain, V., Zhang, H.: Robust 3D shape correspondence in the spectral domain. In: IEEE International Conference on Shape Modeling and Applications 2006 (SMI 2006), p. 19, June 2006Google Scholar
  13. 13.
    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
  14. 14.
    Lombaert, H., Arcaro, M., Ayache, N.: Brain transfer: spectral analysis of cortical surfaces and functional maps. Inf. Process. Med. Imaging 24, 474–487 (2015)Google Scholar
  15. 15.
    Lombaert, H., Grady, L., Pennec, X., Ayache, N., Cheriet, F.: Spectral demons- image registration via global spectral correspondence. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) Computer Vision - ECCV 2012. LNCS, vol. 7573, pp. 30–44. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Nielsen, A.: The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data. IEEE Trans. Image Process. 16(2), 463–478 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Nielsen, A.A., Conradsen, K., Simpson, J.J.: Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: new approaches to change detection studies. remote Sens. Environ. 64(1), 1–19 (1998)CrossRefGoogle Scholar
  18. 18.
    Pazos, V., Cheriet, F., Danserau, J., Ronsky, J., Zernicke, R.F., Labelle, H.: Reliability of trunk shape measurements based on 3-D surface reconstructions. Eur. Spine J. 16(11), 1882–1891 (2007)CrossRefGoogle Scholar
  19. 19.
    Reuter, M.: Hierarchical shape segmentation and registration via topological features of laplace-Beltrami eigenfunctions. Int. J. Comput. Vis. 89(2–3), 287–308 (2009)Google Scholar
  20. 20.
    Reuter, M., Wolter, F.E., Peinecke, N.: Laplace-spectra as fingerprints for shape matching. In: Proceedings of the 2005 ACM Symposium on Solid and Physical Modeling, SPM 2005, pp. 101–106. ACM, New York (2005)Google Scholar
  21. 21.
    Richards, B.S., Bernstein, R.M., D’Amato, C.R., Thompson, G.H.: Standardization of criteria for adolescent idiopathic scoliosis brace studies: SRS committee on bracing and nonoperative management. Spine 30(18), 2068–2075 (2005). Discussion 2076–2077CrossRefGoogle Scholar
  22. 22.
    Seoud, L., Dansereau, J., Labelle, H., Cheriet, F.: Multilevel analysis of trunk surface measurements for noninvasive assessment of scoliosis deformities. Spine 37(17), E1045–E1053 (2012)CrossRefGoogle Scholar
  23. 23.
    Tones, M., Moss, N., Polly, D.W.: A review of quality of life and psychosocial issues in scoliosis. Spine 31(26), 3027–3038 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ola Ahmad
    • 1
    • 2
  • Herve Lombaert
    • 3
  • Stefan Parent
    • 1
    • 2
  • Hubert Labelle
    • 1
    • 2
  • Jean Dansereau
    • 2
    • 4
  • Farida Cheriet
    • 2
    • 4
  1. 1.Université de MontréalMontréalCanada
  2. 2.Centre de Recherche du CHU Sainte-JustineMontréalCanada
  3. 3.INRIASophia-antipolisFrance
  4. 4.École Polytechnique de MontréalMontréalCanada

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