Advertisement

Classification of Osteoporotic Vertebral Fractures Using Shape and Appearance Modelling

  • Paul A. BromileyEmail author
  • Eleni P. Kariki
  • Judith E. Adams
  • Timothy F. Cootes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Osteoporosis Vertebral fracture Shape modelling 

Notes

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.

References

  1. 1.
    Rachner, T., Khosla, S., Hofbauer, L.: Osteoporosis: now and the future. Lancet 377(9773), 1276–1287 (2011)CrossRefGoogle Scholar
  2. 2.
    Adams, J.: Opportunistic identification of vertebral fractures. J. Clin. Densitom. 19(1), 54–62 (2016)CrossRefGoogle Scholar
  3. 3.
    Operational Information for Commissioning: Diagnostic imaging dataset statistical release. Technical report, NHS, UK (2016). http://www.england.nhs.uk/statistics/wp-content/uploads/sites/2/2015/08/Provisional-Monthly-Diagnostic-Imaging-Dataset-Statistics-2016-05-19.pdf
  4. 4.
    Williams, A.L., Al-Busaidi, A., Sparrow, P.J., Adams, J.E., Whitehouse, R.W.: Under-reporting of osteoporotic vertebral fractures on computed tomography. Eur. J. Radiol. 69(1), 179–183 (2009)CrossRefGoogle Scholar
  5. 5.
    Kariki, E., Bromiley, P., Cootes, T., Adams, J.: Opportunistic identification of vertebral fractures on computed radiography: need for improvement. Osteoporos. Int. 27(S2), 621 (2016)Google Scholar
  6. 6.
    Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Comput. Vis. Image Understand. 61(1), 38–59 (1995)CrossRefGoogle Scholar
  7. 7.
    Brett, A., Miller, C., Hayes, C., Krasnow, J., Ozanian, T., Abrams, K., Block, J., van Kuijk, C.: Development of a clinical workflow tool to enhance the detection of vertebral fractures. Spine 34(22), 2437–2443 (2009)CrossRefGoogle Scholar
  8. 8.
    Roberts, M., Cootes, T., Adams, J.: Vertebral morphometry: semiautomatic determination of detailed shape from dual-energy X-ray absorptiometry images using active appearance models. Invest. Radiol. 41(12), 849–859 (2006)CrossRefGoogle Scholar
  9. 9.
    Roberts, M.G., Cootes, T.F., Adams, J.E.: Automatic location of vertebrae on DXA images using random forest regression. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 361–368. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33454-2_45CrossRefGoogle Scholar
  10. 10.
    Lindner, C., Bromiley, P., Ionita, M., Cootes, T.: Robust and accurate shape model matching using random forest regression-voting. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1862–1874 (2015)CrossRefGoogle Scholar
  11. 11.
    Bromiley, P., Adams, J., Cootes, T.: Localization of vertebrae on DXA VFA images using constrained local models with random forest regression voting. In: Proceedings of 20th International Bone Densitometry Workshop - IBDW 2014 (2014). J. Orthop. Translat., vol. 2, pp. 227–228Google Scholar
  12. 12.
    Bromiley, P., Adams, J., Cootes, T.: Localisation of vertebrae on DXA images using constrained local models with random forest regression voting. In: Yao, J., et al. (eds.) CSI 2014. LNCVB, vol. 20, pp. 159–171. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-14148-0_14CrossRefGoogle Scholar
  13. 13.
    Bromiley, P.A., Adams, J.E., Cootes, T.F.: Automatic localisation of vertebrae in DXA images using random forest regression voting. In: Vrtovec, T., Yao, J., Glocker, B., Klinder, T., Frangi, A., Zheng, G., Li, S. (eds.) CSI 2015. LNCS, vol. 9402, pp. 38–51. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-41827-8_4CrossRefGoogle Scholar
  14. 14.
    Bromiley, P.A., Kariki, E.P., Adams, J.E., Cootes, T.F.: Fully automatic localisation of vertebrae in CT images using random forest regression voting. In: Yao, J., Vrtovec, T., Zheng, G., Frangi, A., Glocker, B., Li, S. (eds.) CSI 2016. LNCS, vol. 10182, pp. 51–63. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-55050-3_5CrossRefGoogle Scholar
  15. 15.
    Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)CrossRefGoogle Scholar
  16. 16.
    Griffith, J.: Identifying osteoporotic vertebral fracture. Quant. Imaging Med. Surg. 5(4), 592–602 (2015)Google Scholar
  17. 17.
    Jensen, G., McNair, P., Boesen, J., Hegedus, V.: Validity in diagnosing osteoporosis. Observer variation in interpreting spinal radiographs. Eur. J. Radiol. 4(1), 1–3 (1984)Google Scholar
  18. 18.
    Genant, H., Wu, C., Kuijk, C., Nevitt, M.: Vertebral fracture assessment using a semi-quantitative technique. J. Bone Miner. Res. 8(9), 1137–1148 (1993)CrossRefGoogle Scholar
  19. 19.
    Jiang, G.: Diagnosis of vertebral fracture using an ABQ method. Osteoporos. Rev. 18(3), 14–18 (2010)Google Scholar
  20. 20.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  21. 21.
    McCloskey, E., Selby, P., de Takats, D., Bernard, J., Davies, M., Robinson, J., Francis, R., Adams, J., Pande, K., Beneton, M., Jalava, T., Loyttyniemi, E., Kanis, J.: Effects of clodronate on vertebral fracture risk in osteoporosis: a 1-year interim analysis. Bone 28(3), 310–315 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Paul A. Bromiley
    • 1
    Email author
  • Eleni P. Kariki
    • 2
  • Judith E. Adams
    • 2
  • Timothy F. Cootes
    • 1
  1. 1.Centre for Imaging Sciences, School of Health SciencesUniversity of ManchesterManchesterUK
  2. 2.Radiology and Manchester Academic Health Science CentreCentral Manchester University Hospitals NHS Foundation TrustManchesterUK

Personalised recommendations