Abstract
We describe a method for automatic detection and localisation of vertebrae in clinical images that was designed to avoid making a priori assumptions of how many vertebrae are visible. Multiple random forest regressors were trained to identify vertebral end-plates, providing estimates of both the location and pose of the vertebrae. The highest-weighted responses from each model were combined using a Hough-style voting array. A graphical approach was then used to extract contiguous sets of detections representing neighbouring vertebrae, by finding a path linking modes of high weight, subject to pose constraints. The method was evaluated on 320 lateral dual-energy X-ray absorptiometry spinal images with a high prevalence of osteoporotic vertebral fractures, and detected 92 % of the vertebrae between T7 and L4 with a mean localisation error of 2.36 mm. When used to initialise a constrained local model segmentation of the vertebrae, the method increased the incidence of fit failures from 1.5 to 2.1 % compared to manual initialisation, and produced no difference in fracture classification using a simple classifier.
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Notes
- 1.
This was performed as a separate step for implementation reasons. We have not yet investigated the possibility of performing the search for all RF regressors using a combined array.
- 2.
44 patients from a previous study [15]; 80 female subjects in an epidemiological study of a UK cohort born in 1946; 196 females attending a local clinic for DXA BMD measurement, for whom the referring physician had requested VFA (approved by the local ethics committee).
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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. The views expressed in this publication are those of the authors and not necessarily those of the Department of Health or Wellcome Trust.
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Bromiley, P.A., Adams, J.E., Cootes, T.F. (2016). Automatic Localisation of Vertebrae in DXA Images Using Random Forest Regression Voting. In: Vrtovec, T., et al. Computational Methods and Clinical Applications for Spine Imaging. CSI 2015. Lecture Notes in Computer Science(), vol 9402. Springer, Cham. https://doi.org/10.1007/978-3-319-41827-8_4
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