2024, Vol. 28 ›› Issue (2): 252-257

Computer aided design of 3D dental segmentation and its application scenarios

Cui Jiali1, Huang Minhui1, Liu Donglin2, Jia Ruiming1, Li Han1   

Abstract: BACKGROUND: Traditional 3D dental segmentation methods usually utilize predefined spatial geometric features, such as curvature and normal vectors, as the reference information for tooth segmentation.
OBJECTIVE: To propose an algorithm for complex 3D dental segmentation and deeply explore the correlation between segmentation results and application scenarios.
METHODS: A 3D dental segmentation algorithm based on dual stream extraction of structural features and spatial features was established, and the modular design of split flow was used to avoid feature confusion. Among them, the attention mechanism on the structural feature flow was used to capture the fine-grained semantic information required for tooth segmentation, and the Tran Net based on the spatial feature flow was used to ensure the robustness of the model to complex tooth and jaw segmentation. This algorithm verified its effectiveness and reliability based on clinical datasets including healthy dental jaws and complex dental jaws such as missing teeth, malocclusion and dentition crowding. The segmentation performance of the model was measured in terms of overall accuracy, mean intersection over union, and directional cut discrepancy.
RESULTS AND CONCLUSION: The overall segmentation accuracy of this algorithm in the clinical data set is 97.08%, and the segmentation effect is superior to that of other competitive methods from the qualitative and quantitative perspectives. It is verified that the structural feature flow designed in this paper can extract more precise local details of tooth shape from coordinate and normal information by constructing an attention aggregation mechanism, and the spatial feature flow designed in this paper can ensure the robustness of the model to complex teeth such as missing teeth, dislocated teeth, and crowded dentition by constructing a transformation network (Tran Net). Therefore, this tooth segmentation algorithm is highly reliable for clinicians' practical reference.

Key words: oral disease prevention, orthodontics, neural network, 3D dental segmentation, reliability