  Computerized registration between maxillofacial cone-beam computed tomography
(CT) images and a scanned dental model is an essential prerequisite in surgical
planning for dental implants or orthognathic surgery. We propose a novel method
that performs fully automatic registration between a cone-beam CT image and an
optically scanned model. To build a robust and automatic initial registration
method, our method applies deep-pose regression neural networks in a reduced
domain (i.e., 2-dimensional image). Subsequently, fine registration is
performed via optimal clusters. Majority voting system achieves globally
optimal transformations while each cluster attempts to optimize local
transformation parameters. The coherency of clusters determines their candidacy
for the optimal cluster set. The outlying regions in the iso-surface are
effectively removed based on the consensus among the optimal clusters. The
accuracy of registration was evaluated by the Euclidean distance of 10
landmarks on a scanned model which were annotated by the experts in the field.
The experiments show that the proposed method's registration accuracy, measured
in landmark distance, outperforms other existing methods by 30.77% to 70%. In
addition to achieving high accuracy, our proposed method requires neither
human-interactions nor priors (e.g., iso-surface extraction). The main
significance of our study is twofold: 1) the employment of light-weighted
neural networks which indicates the applicability of neural network in
extracting pose cues that can be easily obtained and 2) the introduction of an
optimal cluster-based registration method that can avoid metal artifacts during
the matching procedures.
