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.
Original language | English |
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Title of host publication | Recent Advances in Computational Methods and Clinical Applications for Spine Imaging |
Editors | Jianhua Yao, Ben Glocker, Tobias Klinder, Shuo Li |
Place of Publication | Switzerland |
Publisher | Springer Nature |
Pages | 159-171 |
Number of pages | 13 |
Volume | 20 |
ISBN (Print) | 978-3-319-14147-3 |
DOIs | |
Publication status | Published - 14 Sept 2014 |
Event | MICCAI Workshop on Computational Methods and Clinical Applications for Spine Imaging (CSI 2014) - Boston, USA Duration: 14 Sept 2014 → 14 Sept 2014 |
Publication series
Name | Lecture Notes in Computational Vision and Biomechanics |
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Conference
Conference | MICCAI Workshop on Computational Methods and Clinical Applications for Spine Imaging (CSI 2014) |
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City | Boston, USA |
Period | 14/09/14 → 14/09/14 |
Fingerprint
Dive into the research topics of 'Localisation of Vertebrae on DXA Images using Constrained Local Models with Random Forest Regression Voting'. Together they form a unique fingerprint.Impacts
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ASPIRE™: Using Machine Learning to detect undiagnosed fractures in patients with osteoporosis
Bromiley, P. (Participant), Cootes, T. (Participant), Kariki, E. (Participant) & Adams, J. (Participant)
Impact: Technological impacts, Health and wellbeing