An automated tool to identify vertebral fractures in various imaging modalities
Funders: NIHR.
Overview
Osteoporosis is a condition in which patients have too little bone, and so are more prone to suffering fractures, most commonly in the spine, wrist and hip. These lead to pain and deformity and often death. Osteoporosis affects 1 in 2 women and 1 in 5 men over age 50 years. Vertebral fractures are the most common fractures in osteoporosis, and if present indicate that the patient is at significantly increased risk of future fractures and should be treated. However, over 50% of vertebral fractures are not associated with symptoms and so their presence may not be suspected; and are often not reported if present on various imaging techniques.
Participants
This project was a collaboration between:
During the project we developed technology for locating and analysing vertebra in CT images. This was be integrated into an automated tool for identifying vertebral fractures, developed by Optasia-Medical. The project worked towads adoption within the NHS.
Impact
By identifying subjects with vertebral fractures who would benefit from referral for further assessment for osteoporosis the system should ultimately reduce the number of fractures, including the numbers of potentially fatal hip fractures.
Related work
This project builds on our extensive earlier work on identifying vertebral fractures.
Publications
P. A. Bromiley, J. E. Adams, and T. F. Cootes, "Localisation of Vertebrae on DXA Images Using Constrained Local Models with Random Forest Regression Voting", Recent Advances in Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics Vol.20, pp.159-171, 2015.
.A.Bromiley, J.E.Adams and T.F.Cootes, "Automatic Localisation of Vertebrae in DXA Images using Random Forest Regression Voting", Proc. 3rd Workshop and Challenge on Computational Methods and Clinical Applications in Spine Imaging (CSI), 2015, pp.159-171
P.Bromiley et al. "Multi-point Regression Voting for Shape Model Matching", Int. Conf. on Medical Image Understanding and Analysis 2016
P.A.Bromiley, E.P.Kariki, J.E.Adams and T.F.Cootes, "Fully Automatic Localisation of Vertebrae in CT Images using Random Forest Regression Voting", Proc. Computational Methods and Clinical Applications in Spine Imaging (CSI) 2016, pp.51-58
P.A. Bromiley, E.P.Kariki, J.E.Adams, T.F.Cootes,"Classification of Osteoporotic Vertebral Fractures using Shape and Appearance Modelling", Proc. Computations Methods and Clinical Applications in Musculoskeletal Imaging, MICCAI 2017
P.Bromiley, E.Kariki and T.F.Cootes, "Error Estimation for Appearance Model Segmentation of Musculoskeletal Structures using Multiple, Independent Sub-models", International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging, 2018
