Replacing the humeral head with an artificial one via surgery is one of the options to treat glenohumeral osteoarthritis. Thus, designing the artificial humeral head is an important step to alter clinical outcomes. In order to design the artificial humeral head, the individual variety of the humeral heads should be investigated. The statistical shape model (SSM) has been attracting considerable attention to grasp 3-D shape variety; however, no method to derive the SSM of humeral heads has been studied. This paper proposes a method to construct an SSM of humeral heads based on the anatomical landmarks in shoulder computed tomography (CT) images. The proposed method consists of three steps: humeral head extraction, position and pose alignment, and finally, principle component analysis. The method was applied to 22 male subjects with leave-one-out cross validation. The proposed method obtained an average Dice coefficient of 0.92 to represent the individual shape using the constructed SSM. According to shape analysis of the humeral head, we found that the thickness of the humeral head was associated with individual characteristics of the humeral head. Therefore, it can be said that this study can provide patient-specific design of an artificial humeral head.
Automated Segmentation of the Liver from 3D CT Images Using Probabilistic Atlas and Multi-level Statistical Shape Model