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International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging

MSKI 2017: Computational Methods and Clinical Applications in Musculoskeletal Imaging pp 133–147Cite as

Classification of Osteoporotic Vertebral Fractures Using Shape and Appearance Modelling

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Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 10734))

Abstract

Osteoporotic vertebral fractures (VFs) are under-diagnosed, creating an opportunity for computer-aided, opportunistic fracture identification in clinical images. VF diagnosis and grading in clinical practice involves comparisons of vertebral body heights. However, machine vision systems can provide a high-resolution segmentation of the vertebrae and fully characterise their shape and appearance, potentially allowing improved diagnostic accuracy. We compare approaches based on vertebral heights to shape/appearance modelling combined with k-nearest neighbours and random forest (RF) classifiers, on both dual-energy X-ray absorptiometry images and computed tomography image volumes. We demonstrate that the combination of RF classifiers and appearance modelling, which is novel in this application, results in a significant (up to 60% reduction in false positive rate at 80% sensitivity) improvement in diagnostic accuracy.

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Acknowledgements

This publication presents independent research supported by the Health Innovation Challenge Fund (grant no. HICF-R7-414/WT100936), a parallel funding partnership between the Department of Health and Wellcome Trust, and by the NIHR Invention for Innovation (i4i) programme (grant no. II-LB_0216-20009). The views expressed are those of the authors and not necessarily those of the NHS, NIHR, the Department of Health or Wellcome Trust. The authors acknowledge the invaluable assistance of Mrs Chrissie Alsop, Mr Stephen Capener, Mrs Imelda Hodgkinson, Mr Michael Machin, and Mrs Sue Roberts, who performed the manual annotations.

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Authors and Affiliations

  1. Centre for Imaging Sciences, School of Health Sciences, University of Manchester, Manchester, UK

    Paul A. Bromiley & Timothy F. Cootes

  2. Radiology and Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK

    Eleni P. Kariki & Judith E. Adams

Authors
  1. Paul A. Bromiley
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  2. Eleni P. Kariki
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  3. Judith E. Adams
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  4. Timothy F. Cootes
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Corresponding author

Correspondence to Paul A. Bromiley .

Editor information

Editors and Affiliations

  1. Imperial College London, London, UK

    Ben Glocker

  2. National Institutes of Health, Bethesda, MD, USA

    Jianhua Yao

  3. University of Ljubljana, Ljubljana, Slovenia

    Tomaž Vrtovec

  4. University of Sheffield, Sheffield, UK

    Alejandro Frangi

  5. University of Bern, Bern, Switzerland

    Guoyan Zheng

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Bromiley, P.A., Kariki, E.P., Adams, J.E., Cootes, T.F. (2018). Classification of Osteoporotic Vertebral Fractures Using Shape and Appearance Modelling. In: Glocker, B., Yao, J., Vrtovec, T., Frangi, A., Zheng, G. (eds) Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2017. Lecture Notes in Computer Science(), vol 10734. Springer, Cham. https://doi.org/10.1007/978-3-319-74113-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-74113-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74112-3

  • Online ISBN: 978-3-319-74113-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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