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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References
Rachner, T., Khosla, S., Hofbauer, L.: Osteoporosis: now and the future. Lancet 377(9773), 1276–1287 (2011)
Adams, J.: Opportunistic identification of vertebral fractures. J. Clin. Densitom. 19(1), 54–62 (2016)
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
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)
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)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Comput. Vis. Image Understand. 61(1), 38–59 (1995)
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)
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)
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_45
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)
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. 227–228
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_14
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_4
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_5
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)
Griffith, J.: Identifying osteoporotic vertebral fracture. Quant. Imaging Med. Surg. 5(4), 592–602 (2015)
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)
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)
Jiang, G.: Diagnosis of vertebral fracture using an ABQ method. Osteoporos. Rev. 18(3), 14–18 (2010)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
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)
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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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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