This study aims to use deep learning to perform automatic scapula bone segmentation and to simultaneously synthetize CT acquisition. With funding from the CCMBM Pilot and Feasibility Grant Program, the proposed study builds a novel translational platform to revolutionize shoulder MR images in research studies, but also is paradigm-shifting in that it may provide a first step towards a more quantitative approach to surgical planning and patient management.
Methodology Needed to Obtain Bony and Soft Tissue Information
Scapular bone shape is an important determinant in surgical planning and predictor of post-operative outcomes for patients with shoulder instability and shoulder osteoarthritis. Currently, clinical evaluation of scapular bone shape is performed on a three-dimensional (3D) computed tomography (CT) scan, while a magnetic resonance imaging (MRI) scan is obtained to evaluate the soft tissue surrounding the shoulder. There is a clear clinical and research need for a methodology to obtain bony and soft tissue information from a single imaging study in an accurate, repeatable and fully automated fashion. Read more >>
