Tooth and Pulp Chamber Automatic Segmentation with Artificial Intelligence Network and Morphometry Method in Cone-beam CT

Abstract

This study aims to extract teeth and alveolar bone structures in CBCT images automatically, which is a key step in CBCT image analysis in the field of stomatology. In this study, semantic segmentation was used for automatic segmentation. Five marked classes of CBCT images were input for U-net neural network training. Tooth hard tissue (including enamel, dentin, and cementum), dental pulp cavity, cortical bone, cancellous bone, and other tissues were marked manually in each class. The output data were from different regions of interest. The network configuration and training parameters were optimized and adjusted according to the prediction effect. This method can be used to segment teeth and peripheral bone structures using CBCT. The time of the automatic segmentation process for each CBCT was less than 13 min. The Dice of the evaluation reference image was 98 %. The U-net model combined with the watershed method can effectively segment the teeth, pulp cavity, and cortical bone in CBCT images. It can provide morphological information for clinical treatment.