  This paper presents a fully automatic registration method of dental cone-beam
computed tomography (CBCT) and face scan data. It can be used for a digital
platform of 3D jaw-teeth-face models in a variety of applications, including 3D
digital treatment planning and orthognathic surgery. Difficulties in accurately
merging facial scans and CBCT images are due to the different image acquisition
methods and limited area of correspondence between the two facial surfaces. In
addition, it is difficult to use machine learning techniques because they use
face-related 3D medical data with radiation exposure, which are difficult to
obtain for training. The proposed method addresses these problems by reusing an
existing machine-learning-based 2D landmark detection algorithm in an
open-source library and developing a novel mathematical algorithm that
identifies paired 3D landmarks from knowledge of the corresponding 2D
landmarks. A main contribution of this study is that the proposed method does
not require annotated training data of facial landmarks because it uses a
pre-trained facial landmark detection algorithm that is known to be robust and
generalized to various 2D face image models. Note that this reduces a 3D
landmark detection problem to a 2D problem of identifying the corresponding
landmarks on two 2D projection images generated from two different projection
angles. Here, the 3D landmarks for registration were selected from the
sub-surfaces with the least geometric change under the CBCT and face scan
environments. For the final fine-tuning of the registration, the Iterative
Closest Point method was applied, which utilizes geometrical information around
the 3D landmarks. The experimental results show that the proposed method
achieved an averaged surface distance error of 0.74 mm for three pairs of CBCT
and face scan datasets.
