Automatic Segmentation of the Humerus Region in 3-D Shoulder CT Images Using U-Net

抄録

In order to diagnose osteoarthritis of the shoulder joint, the 3-D shape of the humerus provides the essential information. Also, the segmented region can be utilized for analyzing individual variety of the 3-D shape between normal and anomaly, etc. Since now, there is no study which automatically segments the humerus region from computed tomography (CT) images. U-Net is a fully-convolutional network architecture, and has been applied to some image segmentation problems. This research introduces U-Net architecture to automatically segment the humerus region in shoulder CT images. To validate the proposed method, it has been applied to 19 male subjects. The method achieved 0.946 Dice coefficient, which demonstrates that it successfully segmented the humerus region with a high level of accuracy and precision.