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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2300596


Accurate estimation of 6D tooth pose in 3D intra oral scans for dental applications using deep learning


Author(s):  Wanghui DING, Kaiwei SUN, Mengfei YU, Hangzheng LIN, Yang FENG, Jianhua LI, Zuozhu LIU

Affiliation(s):  Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou 310000, China; more

Corresponding email(s):   godson888@zju.edu.cn, zuozhuliu@intl.zju.edu.cn

Key Words:  Artificial intelligence, Digital dentistry, Deep learning, Orthodontics, Tooth pose, Neural network


Wanghui DING, Kaiwei SUN, Mengfei YU, Hangzheng LIN,Yang FENG, Jianhua LI, Zuozhu LIU. Accurate estimation of 6D tooth pose in 3D intra oral scans for dental applications using deep learning[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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Abstract: 
A critical step in digital dentistry is to accurately and automatically characterize the orientation and position of individual teeth; this can subsequently be used for treatment planning and simulation in orthodontic tooth alignment. This problem remains challenging because the geometric features of different teeth are complicated and vary significantly, while a reliable large-scale dataset is yet to be constructed. This paper proposes a novel method for automatic tooth orientation estimation by formulating it as a six degrees-of-freedom (6D) tooth pose estimation task. Regarding each tooth as a three-dimensional (3D) point cloud, we design a deep neural network with a feature extractor backbone and a two-branch estimation head for tooth pose estimation. Our model, trained with a novel loss function on the newly collected large-scale dataset (10,393 patients with 280,611 intraoral tooth scans), achieves an average Euler angle error of only 4.78-5.979° and a translation L-1 error of 0.663 mm on a hold-out set of 2598 patients (77,870 teeth). The comprehensive experiments further show that 98.29% of the estimations produce a mean angle error of less than 15°, which is acceptable for many clinical and industrial applications. To the best of our knowledge, we present the first work for automatic 6D tooth pose estimation using deep learning, demonstrating the strong potential of artificial intelligence in future digital dentistry.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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