Cone beam computed tomography (CBCT) has played an important role in the diagnosis of oral diseases. However, it remains a very important research issue in stomatology to obtain accurate information of teeth from complex original scanning images. Because of the complexity of the human oral environment, various human tissues, especially dental bones, seriously affect the teeth segmentation algorithm. This problem is particularly obvious in the image regions around the roots of the teeth. Moreover, tooth fillings also seriously interfere with the operation of the algorithm. Therefore, compared with other image segmentation algorithms, the current core requirement of teeth image segmentation algorithms is higher segmentation accuracy. The algorithm should have the ability to accurately segment targets out of objects with similar gray levels in various interferences. Conventional image segmentation algorithms such as U-net algorithm and Mask R-CNN algorithm cannot achieve the required segmentation accuracy. The main purpose of this paper is to research and propose a method of teeth image segmentation and three-dimensional (3D) reconstruction from CBCT images. We construct CBCT image dataset from original images and realize 3D teeth image reconstruction through programming and training.

We use a novel segmentation algorithm based on an improved U-net network, for which the spatial attention mechanism is introduced into conventional artificial neural network. Spatial attention mechanism can effectively enhance the features of teeth and suppress unimportant features to improve the recognition ability of the network, which can help us to segment teeth more accurately. Combined with the isosurface extraction algorithm, the accurate segmentation and three-dimensional reconstruction of teeth CBCT images are realized. First, we use Labelme software with the patient data collected from medical institutions to create a human oral CBCT dataset, and then we train the model using the improved U-net network. Second, we use the trained model to segment the images to obtain images that only retain the teeth information. Third, the results are reconstructed to create a three-dimensional model of the teeth by Marching Cubes algorithm.

We applied the new method proposed in this paper to the CBCT tomographic images, and obtained the image segmentation and reconstruction results. We actually tested the segmentation and recognition effect of the algorithm, and got the MIoU parameters of the model, as well as the image segmentation and reconstruction effect. The MIoU parameters of the improved U-net network and the original U-net network were compared. It could be seen that the improved algorithm has 0.064 higher recognition effect on teeth, 0.002 higher recognition effect on background, and 0.033 higher overall recognition effect. The quantitative analysis of MIoU parameters showed that the improved algorithm has better recognition ability. Compared with the actual teeth image segmentation recognition effect, it could be seen that the newly proposed algorithm in this paper has better recognition effect and can effectively recognize the image under the influence of tooth fillings. Compared with the 3D model reconstructed by MicroDicom Viewer software, it could be seen that the teeth had been effectively reconstructed, and the contour, relative position and the roots of the teeth can be clearly seen. In contrast, the roots of the teeth are difficult to distinguish in the original CBCT data, because they are often close to the jaw. In addition, the reconstructed image of this experiment had clear and complete contour, and there was no fault. The above experiments used the CBCT data of an impacted wisdom tooth patient. After applying our reconstruction algorithm to the data, the patient’s oral condition could be clearly seen from the side. It was obvious that the growth direction of the innermost two teeth of the patient is completely different from that of the normal teeth, especially the impacted tooth below. In summary, the experimental results showed that the method proposed in this paper has a good application effect in the field of oral medicine.

The experimental results showed that the improved network can accurately recognize teeth from the original image, and has better recognition ability than the original U-net network; besides, the novel network can accurately recognize teeth under the interference of tooth fillings. Compared with the 3D model reconstructed by MicroDicom Viewer software, the final reconstructed 3D model has a clearer and more complete contour, and can more realistically reproduce the patient’s tooth conditions. The experimental results also showed that this method can effectively extract tooth information, which is helpful to the diagnosis and treatment of oral diseases, especially tooth diseases. The trial feedback of this method testing in the cooperative hospital is quite terrific, and the effect of helping stomatologists to diagnose and treat is significant. The research findings of this paper are expected to make greater contributions in the field of oral health in the future.