About the Project
This project will develop and validate CNNs that can identify VFs in clinical images and could be incorporated into ASPIRE(TM) to improve accuracy and efficiency. Regional CNN regressors that can identify vertebral body outlines will be combined with statistical shape models of the spine, the latter providing a constraint that allows training on relatively small numbers (~1000s) of images whilst dealing with the highly repetitive nature of spinal structures. Through the course of the ASPIRE(TM) project we have acquired a database of over 2000 CT image volumes, each with manual annotations of the vertebral body outlines and diagnoses of spinal pathology. The algorithms will be trained, tested and validated using these images, allowing comparison to the accuracy of our current technology. The aim will be to improve the accuracy and efficiency of the technology used in ASPIRE(TM), reducing the cost of the service to the NHS.
The student will join a well-established, interdisciplinary team including academic, clinical and industrial partners. They will gain extensive knowledge of state-of-the-art computer vision algorithm development for clinical imaging problems. The student will have the opportunity to gain experience of working with an industrial partner, and the translational skills required to transfer research results between the academic and industrial environments. They will also have the opportunity to analyse results from the on-going feasibility studies of ASPIRE(TM), and to feed into an associated health economic study. Finally, they will work with our clinical partners from the Manchester Royal Infirmary and gain extensive knowledge of osteoporosis, particularly the clinical imaging, diagnosis and epidemiology of vertebral fractures and other spinal pathologies.
P.A. Bromiley, J. Adams and T.F. Cootes. Localisation of Vertebrae on DXA Images using Constrained Local Models with Random Forest Regression Voting. Lecture Notes in Computational Vision and Biomechanics 20, p. 159-171, 2015.
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. Lecture Notes in Computer Science 10182. p. 51-63, 2017.
P.A. Bromiley, E.P. Kariki, J.E. Adams and T.F. Cootes. Classification of Osteoporotic Vertebral Fractures using Shape and Appearance Modelling. Lecture Notes in Computer Science 10734, p. 133-147, 2018.
J. Staal, E. Kariki, R. Hyatt, M.K. Javaid, E. Russell, T. O'Neill, K. Poole, D. Chappell, R. Rajak. Multi-site opportunistic diagnosis of vertebral fragility fractures in computed tomography scans. Osteoporosis International 29(S2) p. S646, 2018.
Why not add a message here
Based on your current searches we recommend the following search filters.
Based on your current search criteria we thought you might be interested in these.