面向CBCT图像的金字塔微分同胚变形牙齿网格重建方法

Reconstructing tooth meshes by pyramid diffeomorphic deformation from CBCT images

从锥形束计算机断层扫描(cone-beam computed tomography,CBCT)中生成准确、高质量的牙齿网格是数字牙科常用的一种计算机辅助技术。然而,现有的基于实例分割的方法需要进行复杂的后处理和大量手动修正才能生成符合后续使用要求(如有限元分析)的牙齿网格。通过学习由微分同胚变形生成多分辨率的牙齿网格,直接用CBCT图像生成高质量的牙齿网格。采用经典的两阶段网络,第1阶段,用改进后的牙齿检测网络准确定位和裁剪牙齿;第2阶段,基于微分同胚变形的金字塔流将球面网格从低分辨率变形为高分辨率,使得生成的牙齿网格高效逼近目标网格。实验结果表明,所提方法在各评估指标上和重建牙齿表面的几何质量上均优于现有先进方法。

Accurate and high-quality shape generation of individual teeth from cone-beam computerized tomography(CBCT)is essential for computer-aided dentistry.Existing methods need post-process to extract isosurfaces and the output meshes cannot be directly used as the input for most subsequent applications(such as finite element analysis(FEA).In this paper,we propose the network that directly learns the multi-resolution mesh guided by diffeomorphic deformation.Overall,our solution is a classic two-stage schema widely used in tooth reconstruction.Firstly,we adopt a revised anchor-free detector to locate each individual tooth with high precision.Then,we design the top-to-bottom flows from the multi-level features of each individual,referred to as pyramid flows,to predict diffeomorphic deformation from a sphere to a detailed tooth.Finally,we validate the effectiveness and efficiency of the proposed approach by comparing with the previous segmentation methods and other explicit surface learning-based methods in the experiment.