Automatic selection of tooth seed point by graph convolutional network

Abstract

Objective: With the rapid development of 3D digital imaging technology, increasing applications of computer-aided diagnosis and treatment in oral restoration have gradually become the development trend in this field. A key step in orthodontics is to separate the teeth on the digital 3D dental model. The selection of tooth seed points is crucial in the tooth segmentation method commonly used in computer orthodontics. Most orthodontic software in the industry adopts a segmentation method that requires interactive marking, and the seed point of each tooth is selected on the 3D dental model by human-computer interaction. The efficiency is low. To solve this problem, an automatic selection method for tooth seed points based on feature-steered graph convolutional network (FeaStNet) is proposed. Method: The seed point position and final segmentation effect of each tooth type are analyzed, and a unified rule is established. A seed point dataset of a dental model is built. A new multiscale graph convolution architecture is constructed using feature-steered graph convolutions to identify the feature information on the 3D dental model. The depth of the network model is deepened to fit the characteristics of the teeth. Training is conducted to adjust parameters, and a multiscale network structure is used to find specific seed points. The prediction model is evaluated using the mean squared difference loss function to improve the accuracy. The feature points identified through the network are regarded as the basic points to find the point closest to the base point on the dental model and set it as the seed point. If the seed point position is accurate, then the teeth are separated from the gums in accordance with the seed point. For the result of inaccurate seed point position, the position of the seed point is corrected by manual operation first and then segmentation is performed. The dental model is simplified to improve the training speed. The simplified model is used to establish an experimental dataset and specify the position of the seed points of different types of teeth. The 3D dental model is divided into the following information for preservation: the 3D coordinate values of all vertices in the dental model, the adjacency relationship of the vertices in the dental model, and the 3D coordinate values of the tooth seed points. The mean squared difference loss function is used to perform an imbalance check. The loss value decreases rapidly at the beginning of training, converges promptly, and tends to be stable after an oscillation period. Result: The experiment is conducted on a self-built dataset, in which the exact point of the seed point is 88%. In other cases, only the position of a partially inaccurate seed point needs to be adjusted. The base point of the deviation of the teeth is roughly divided into two cases. One is that the seed point of the incisor deviates, and the other is that the molar is not on the tooth surface. After the basic point is acquired, the point closest to the base point on the dental model is determined as a seed point and applied to the orthodontic software to separate the teeth from the gums.The method is simple and fast and has less manual intervention than existing methods. Thus, its work efficiency improved. After the method is applied to the orthodontic software and the hardware platform, the entire segmentation time is accelerated to approximately 7 s. In the path-planning method, the time taken for segmentation is approximately 20 s. The comparison proves that the current method has obvious advantages in speed. Conclusion: The proposed seed-point automatic selection method solves the problem of tooth segmentation requiring interactive marking and automates dental division. It is applicable to all types of dental deformity in patients with tooth model division. The automatic selection of seed points can also be used as a reference for other segmentation methods. In the segmentation of teeth, in addition to the characteristics of seed points, other tooth feature points are important. Future research is suggested to learn additional tooth features, further improve the speed of tooth segmentation, and help doctors improve work efficiency.