Longitudinal Scoliotic Trunk Analysis via Spectral Representation and Statistical Analysis
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.
KeywordsCobb Angle Canonical Correlation Analysis Canonical Variate Radial Basis Function Weighted Adjacency Matrix
This research was funded by the Canadian Institutes of Health Research (grant number MPO 125875). The authors would like to thank Philippe Debanné for revising this paper and the anonymous reviewers for their insightful comments and suggestions.
- 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.Cobb, J.R.: Outline for the study of scoliosis. Am. Acad. Orthop. Surg. Instruct. Lect. 5, 261–275 (1984)Google Scholar
- 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
- 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.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
- 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.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