Clinical tooth segmentation based on local enhancement - Wu, Jipeng; Zhang, Ming; Yang, Delong; Wei, Feng; Xiao, Naian; Shi, Lei; Liu, Huifeng; Shang, Peng (Frontiers in Molecular Biosciences, 2022)
The tooth arrangements of human beings are challenging to accurately observe when relying on dentists’ naked eyes, especially for dental caries in children, which is hard to detect. Cone Beam Computer Tomography (CBCT) is used as an auxiliary method to measure patients’ teeth, including children. However, subjective and irreproducible manual measurements are required during this process, which wastes much time and energy for the dentists. Therefore, a fast and accurate tooth segmentation algorithm that can replace repeated calculations and annotations in manual segmentation has tremendous clinical significance. This paper proposes a local contextual enhancement model for clinical dental CBCT images. The local enhancement model more suitable for dental CBCT images is proposed based on the analysis of the existing contextual models. Then, the local enhancement model is fused into an encoder-decoder framework for dental CBCT images. At last, extensive experiments are conducted to validate our method.