Automatic Segmentation of Internal Tooth Structure from CBCT Images Using Hierarchical Deep Learning

Accurate segmentation of teeth is crucial for effective treatment planning. Previous approaches attempted to segment a tooth as a whole, which has limitations because most treatments involve internal structures of teeth. In this paper, we propose fully automated segmentation of internal tooth structure, including enamel, dentin, and pulp, which is the first attempt to the best of our knowledge. The task is challenging, because a total of 96 classes of tooth structures need to be identified from a CBCT image. We design a 3-stage process of coarse-to-fine segmentation of tooth structures without compromising the original resolution. We propose Dual-Hierarchy U-Net (DHU-Net) in order to capture hierarchical structures of teeth, and to effectively fuse encoder and decoder features from higher and lower hierarchies. Experiments demonstrate that our method outperforms state-of-the-art methods in both tasks of segmenting the whole tooth and internal tooth structure.