Automatic Segmentation of Rotator Cuff Muscles on MR Images Using Deep Learning

Rotator cuff (RC) injuries are a common occurrence affecting millions of people across the globe. Quantitative MRI-based evaluation of RC injuries can aid in early diagnosis and improve the treatment outcome. A crucial step towards developing a quantitative, clinically relevant methods for these patients, is developing reliable automatic techniques for segmentation of RC muscles. In this study, we developed a deep convolutional neural network model to automatically segment RC muscles on T1-weighted MR images. We showed that the proposed deep learning method provides rapid and reliable automatic segmentation of RC muscles, with an accuracy comparable with that of human raters.