An Efficient Method of Tooth Segmentation Under Massive Medical Data

Tian Ma, Yizhou Yang, Yun Li, Zhanli Li, Yuancheng Li

OAI: oai:igi-global.com:309414 • DOI: 10.4018/JDM.309414

To accurately and efficiently complete tooth segmentation from a large amount of oral medical data, the burden of doctors should be reduced. An automatic seed picking method based on 2D projection of the occlusal plane was proposed. First, the authors establish a two-dimensional seed data set for tooth segmentation. Then, this article built a prediction network of teeth seeds based on YOLOv4 to realize the prediction of teeth position as well as the recognition of teeth categories. Finally, according to the statistical optimal seed position, the two-dimensional seeds are calculated and mapped back to the three-dimensional space by the reverse projection transformation method to realize the final picking up of the three-dimensional seeds. Furthermore, combined with the previous work of division line detection, the automatic segmentation of the 3D dental model was realized. The experimental results show that the proposed method has high accuracy and real-time performance, which significantly reduces the burden of human-computer interaction in dental model segmentation.