基于GCNN的CBCT模拟口扫点云数据牙齿分割算法

三维牙模型的获取并实现牙体边界的精准分割,对于口腔正畸及种植对牙齿的诊断和制定后续治疗计划具有重要意义.为了实现单个牙体的精准分割,提出一种基于CBCT数据模拟口扫点云数据实现牙齿自动分割的算法.借助锥形束计算机断层扫描(CBCT)重建出的三维牙模型,通过对牙模型的局部精细与全局粗略结构的深度学习网络,实现单个牙体的精准语义分割.该框架基于图卷积网络(graph convolutional neural networks,GCNN),主要包括2个部分:一是实例分割网络,用于获得牙体的大体形状及相对位置信息;二是细粒度分割网络,用于学习单个牙体的精细细节部分,对分配错误的标签增加惩罚机制,进一步提高了牙体分割精确度.利用文中构建的牙体数据集分别在所提算法、PointNet++和GACNet进行测试,结果表明,所采用的改进GCNN框架可实现精准的三维牙体分割.核心评估指标平均交并比(MIoU)的得分为0.91,优于目前普遍使用的点云语义分割框架PointNet++(MIoU=0.78)和GACNet(MIoU=0.88).实现基于CBCT模拟口扫点云数据的牙齿分割,对于进一步应用临床有重要意义.

Semantic Segmentation of 3D Tooth Model Based on GCNN for CBCT Simulated Mouth Scan Point Cloud Data

The 3 D data acquisition,reconstruction and accurate segmentation of teeth are of great significance for diagnosis and treatment planning in dentistry.A 3 D tooth model was reconstructed based on the cone-beam-CT(CBCT),and an improved GCNN network was proposed to improve the semantic segmentation of each tooth in the 3 D tooth model.Accurate semantic segmentation of individual tooth can be achieved by deep learning of the fine local details as well as rough global structure of each tooth.The framework of the improved network consists of two parts:(1)an example segmentation network,which is used to obtain the general shape and relative position information of each tooth;(2)a fine-grained segmentation network,which is used to learn the fine details of individual tooth.A penalty mechanism for mis-assigned labels was further used to improve the accuracy of tooth segmentation.The results showed that the end-to-end deep learning framework used in this study can achieve accurate segmentation in the 3 D tooth.The mean intersection over union(MIoU)score of the proposed improved GCNN network achieves 0.91,which was much better than that of PointNet++(MIoU:0.78)and GACNet(MIoU:0.88).