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Statistical Shape Modeling of Musculoskeletal Structures and Its Applications

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Computational Radiology for Orthopaedic Interventions

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 23))

Abstract

Statistical shape models (SSM) describe the shape variability contained in a given population. They are able to describe large populations of complex shapes with few degrees of freedom. This makes them a useful tool for a variety of tasks that arise in computer-aided medicine. In this chapter we are going to explain the basic methodology of SSMs and present a variety of examples, where SSMs have been successfully applied.

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References

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Authors and Affiliations

  1. Zuse Institute Berlin (ZIB), Takustr. 7, 14195, Berlin, Germany

    Hans Lamecker & Stefan Zachow

  2. 1000shapes GmbH, Wiesenweg 10, 12247, Berlin, Germany

    Hans Lamecker

Authors
  1. Hans Lamecker
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  2. Stefan Zachow
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Corresponding author

Correspondence to Hans Lamecker .

Editor information

Editors and Affiliations

  1. Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland

    Guoyan Zheng

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

    Shuo Li

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Lamecker, H., Zachow, S. (2016). Statistical Shape Modeling of Musculoskeletal Structures and Its Applications. In: Zheng, G., Li, S. (eds) Computational Radiology for Orthopaedic Interventions. Lecture Notes in Computational Vision and Biomechanics, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-23482-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-23482-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23481-6

  • Online ISBN: 978-3-319-23482-3

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