ZTE segmentation of glenohumeral bone structure using deep learning

Evaluation of 3D bone morphology of the glenohumeral joint is necessary for pre-surgical planning. Zero echo time (ZTE) MRI provides excellent bone contrast, and we developed a deep learning model to perform automated segmentation of major bones (i.e., humerus and others) from ZTE to aid evaluation. Axial ZTE images of normal shoulders (n=31) acquired at 3T were annotated for training with a 2D U-Net, and the trained model was validated with testing data (n=10 normal shoulder, n=6 symptomatic). Testing accuracy was around 80 to 90% (Dice score) for either cohort, except for a few failed cases with very low scores.