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.

翻译：本文提出了一种全自动配准牙科锥形束计算机断层扫描（CBCT）与面部扫描数据的方法。该方法可用于构建三维颌骨-牙齿-面部数字模型平台，适用于三维数字化治疗规划及正颌手术等多种应用场景。由于图像采集方式不同且人体面部表面间对应区域有限，精确融合面部扫描与CBCT图像存在困难。此外，因涉及具有辐射暴露的面部三维医学数据难以获取训练样本，利用机器学习技术也面临挑战。本研究通过复用开源库中基于机器学习的二维关键点检测算法，并开发了一种新型数学算法，该算法可从对应二维关键点信息中识别配对的三维关键点，从而解决上述问题。本研究的主要贡献在于：因采用已知具有鲁棒性且适用于多种二维人脸图像模型的预训练关键点检测算法，所提方法无需标注面部关键点的训练数据。值得注意的是，该方法将三维关键点检测问题简化为在两张不同投影角度生成的二维投影图像中识别对应关键点的二维问题。其中，用于配准的三维关键点选自CBCT与面部扫描环境下几何变化最小的子表面区域。最终配准精调阶段采用迭代最近点方法，利用三维关键点周围的几何信息进行优化。实验结果表明，在三组CBCT与面部扫描数据集上，该方法实现了平均表面距离误差0.74毫米的配准精度。