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Localisation of Vertebrae on DXA Images using Constrained Local Models with Random Forest Regression Voting
P.A. Bromiley, J. Adams and T.F. Cootes
In: Yao, Jianhua; Glocker, Ben; Klinder, Tobias; Li, Shuo. Recent Advances in Computational Methods and Clinical Applications for Spine Imaging: MICCAI Workshop on Computational Methods and Clinical Applications for Spine Imaging (CSI 2014); 14 Sep 2014-14 Sep 2014; Boston, USA. Switzerland: Springer International Publishing; 2014. p. 159-171.
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Abstract
Fractures associated with osteoporosis are a significant public health risk, and one that is likely to increase with an ageing population. However, many osteoporotic vertebral fractures present on images do not come to clinical attention or lead to preventative treatment. Furthermore, vertebral fracture assessment (VFA) typically depends on subjective judgement by a radiologist. The potential utility of computer-aided VFA systems is therefore considerable. Previous work has shown that Active Appearance Models (AAMs) give accurate results when locating landmarks on vertebra in DXA images, but can give poor fits in a substantial subset of examples, particularly the more severe fractures. Here we evaluate Random Forest Regression Voting Constrained Local Models (RFRV-CLMs) for this task and show that, while they lead to slightly poorer median errors than AAMs, they are much more robust, reducing the proportion of fit failures by 68\%. They are thus more suitable for use in computer-aided VFA systems.
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- Honorable Mention: Best Paper Award