Feasibility of Automated Segmentation of 3D Shoulder Muscle Volume via Deep Learning for Rotator Cuff Repair Patients

It has been shown that muscle volume and fat fraction play significant roles in musculoskeletal disorder diagnosis and prognosis. Reliable clinical tools for their evaluation, however, are currently missing. One hurdle is the challenging and laborious manual segmentation process on MR images. We proposed here a deep learning based automated tool for 3D shoulder muscle volume segmentation and achieved accurate segmentation results on clinical MR images from rotator cuff repair patients. The proposed model can be a valuable tool for shoulder muscle volume quantification and subsequent fat fraction analysis to further understand their association with clinical outcomes following shoulder procedures.