计算机辅助设计三维牙颌分割及应用场景  

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

作　　者：崔家礼[1] 黄敏慧 刘东林 贾瑞明[1] 李涵 Cui Jiali;Huang Minhui;Liu Donglin;Jia Ruiming;Li Han(School of Information,North China University of Technology,Beijing 100144,China;School of Basic Medicine,Peking University Health Science Center,Beijing 100191,China)

机构地区：[1]北方工业大学信息学院,北京市100144 [2]北京大学医学部基础医学院,北京市100191

出　　处：《中国组织工程研究》2024年第2期252-257,共6页Chinese Journal of Tissue Engineering Research

基　　金：北京市教委科研计划项目(KM202110009001),项目参与人:崔家礼,贾瑞明,黄敏慧;北航杭州创新研究院钱江实验室开放基金(2020-Y3-A-014),项目负责人:崔家礼。

摘　　要：背景:传统的三维牙颌模型分割方法通常利用预定义的空间几何特征如曲率、法向量等作为牙齿分割的参考信息。目的:提出一种适用于复杂三维牙颌分割的算法并深度挖掘分割结果与应用场景之间的关联性。方法:建立基于结构特征和空间特征双流提取的三维牙颌分割算法,利用分流的模块化设计避免特征混淆。其中,结构特征流上的注意力机制用于捕获牙齿分割所需的细粒度语义信息,空间特征流上的Tran-Net用于保证模型对复杂牙颌分割的鲁棒性。该算法在包含健康牙颌和缺牙、错牙、牙列拥挤等复杂牙颌的临床数据集上验证有效性和可靠性,通过总体精度、均交并比、方向切割差异等评价指标衡量模型的分割性能。结果与结论:该算法在临床数据集上的总体分割精度为97.08%,分割效果从定性和定量的角度均优于其他竞争方法。验证了此次设计的结构特征流,通过构建注意力聚合机制从坐标和法向信息中可提取更精细齿形局部细节,设计的空间特征流通过构建变换网络(Tran-Net)可保证模型对缺牙、错牙、牙列拥挤等复杂牙颌的鲁棒性。因此,该牙齿分割算法对于临床医生实操参考具有较强的可靠性。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 finegrained 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.

关 键 词：口腔疾病预防 牙齿矫正 神经网络 三维牙颌分割 可靠性 

分 类 号：R496[医药卫生—康复医学] R318[医药卫生—临床医学] R181.3