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Articulated Statistical Shape Models of the Spine

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Spinal Imaging and Image Analysis

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 18))

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Abstract

The spine is a complex assembly of rigid vertebrae surrounded by various soft tissues (ligaments, spinal cord, intervertebral discs, etc.). Its motion for a given individual and its shape variations across a population are greatly influenced by this fact. We show in this chapter how statistical shape models can be constructed, used, and analyzed while taking into account the articulated nature of the spine. We begin by defining what articulated models are and how they can be extracted from existing 3D reconstructions or segmented models. As an example, we use data from scoliotic patients that have been reconstructed in 3D using bi-planar radiographs. Articulated models naturally belong to a manifold where conventional statistical tools are not applicable. In this context, a few key concepts allowing the computation of statistical models on Riemannian manifolds are presented. When properly visualized, the resulting statistical models can be quite useful to analyze and compare the shape variations in different groups of patients. Two different approaches to visualization are demonstrated graphically. Finally, another important use of statistical models in medical imaging is to constrain the solution of inverse problems. Articulated models can readily be used in this context, we illustrate this in the context of 3D model reconstruction using partial data. More precisely, we will show the benefits of integrating a simple regularization term based on articulated statistical models to well known algorithms.

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Acknowledgments

We wish to thank M.-A. Drouin whose astute comments greatly improved the manuscript. We would also like to acknowledge Dr. H. Labelle and the Sainte-Justine Hospital (Montreal, Canada) staff for meticulously collecting a large number of high quality 3D reconstructions of the spine over many years and for giving us access to some of these reconstructions. Statistical methods are quite powerful, but they are useless without good data.

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  1. National Research Council, 1200 Montreal Road, Ottawa, Canada

    Jonathan Boisvert

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  1. Jonathan Boisvert
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Correspondence to Jonathan Boisvert .

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Editors and Affiliations

  1. GE Healthcare and University of Western Ontario, London, Ontario, Canada

    Shuo Li

  2. Clinical Center, National Institutes of Health, Bethesda, Maryland, USA

    Jianhua Yao

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Boisvert, J. (2015). Articulated Statistical Shape Models of the Spine. In: Li, S., Yao, J. (eds) Spinal Imaging and Image Analysis. Lecture Notes in Computational Vision and Biomechanics, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-319-12508-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-12508-4_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-12508-4

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