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.
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Notes
- 1.
The Radial Basis Functions (RBF) algorithm [6] is used to interpolate incomplete trunk meshes and to enforce mesh connectivity.
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Acknowledgments
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.
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Ahmad, O., Lombaert, H., Parent, S., Labelle, H., Dansereau, J., Cheriet, F. (2016). Longitudinal Scoliotic Trunk Analysis via Spectral Representation and Statistical Analysis. In: Reuter, M., Wachinger, C., Lombaert, H. (eds) Spectral and Shape Analysis in Medical Imaging. SeSAMI 2016. Lecture Notes in Computer Science(), vol 10126. Springer, Cham. https://doi.org/10.1007/978-3-319-51237-2_7
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DOI: https://doi.org/10.1007/978-3-319-51237-2_7
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