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This paper presents a fully automatic registration method of dental cone-beamcomputed tomography (CBCT) and face scan data. It can be used for a digitalplatform of 3D jaw-teeth-face models in a variety of applications, including 3Ddigital treatment planning and orthognathic surgery. Difficulties in accuratelymerging facial scans and CBCT images are due to the different image acquisitionmethods and limited area of correspondence between the two facial surfaces. Inaddition, it is difficult to use machine learning techniques because they useface-related 3D medical data with radiation exposure, which are difficult toobtain for training. The proposed method addresses these problems by reusing anexisting machine-learning-based 2D landmark detection algorithm in anopen-source library and developing a novel mathematical algorithm thatidentifies paired 3D landmarks from knowledge of the corresponding 2Dlandmarks. A main contribution of this study is that the proposed method doesnot require annotated training data of facial landmarks because it uses apre-trained facial landmark detection algorithm that is known to be robust andgeneralized to various 2D face image models. Note that this reduces a 3Dlandmark detection problem to a 2D problem of identifying the correspondinglandmarks on two 2D projection images generated from two different projectionangles. Here, the 3D landmarks for registration were selected from thesub-surfaces with the least geometric change under the CBCT and face scanenvironments. For the final fine-tuning of the registration, the IterativeClosest Point method was applied, which utilizes geometrical information aroundthe 3D landmarks. The experimental results show that the proposed methodachieved an averaged surface distance error of 0.74 mm for three pairs of CBCTand face scan datasets.

Automatic 3D Registration of Dental CBCT and Face Scan Data using 2D Projection Images