Gan Yangzhou, Xia Zeyang, Xiong Jing, Zhao Qunfei, Hu Ying, Zhang Jianwei
Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and The Chinese University of Hong Kong, Shenzhen 518055, China.
Med Phys. 2015 Jan;42(1):14-27. doi: 10.1118/1.4901521.
A three-dimensional (3D) model of the teeth provides important information for orthodontic diagnosis and treatment planning. Tooth segmentation is an essential step in generating the 3D digital model from computed tomography (CT) images. The aim of this study is to develop an accurate and efficient tooth segmentation method from CT images.
The 3D dental CT volumetric images are segmented slice by slice in a two-dimensional (2D) transverse plane. The 2D segmentation is composed of a manual initialization step and an automatic slice by slice segmentation step. In the manual initialization step, the user manually picks a starting slice and selects a seed point for each tooth in this slice. In the automatic slice segmentation step, a developed hybrid level set model is applied to segment tooth contours from each slice. Tooth contour propagation strategy is employed to initialize the level set function automatically. Cone beam CT (CBCT) images of two subjects were used to tune the parameters. Images of 16 additional subjects were used to validate the performance of the method. Volume overlap metrics and surface distance metrics were adopted to assess the segmentation accuracy quantitatively. The volume overlap metrics were volume difference (VD, mm(3)) and Dice similarity coefficient (DSC, %). The surface distance metrics were average symmetric surface distance (ASSD, mm), RMS (root mean square) symmetric surface distance (RMSSSD, mm), and maximum symmetric surface distance (MSSD, mm). Computation time was recorded to assess the efficiency. The performance of the proposed method has been compared with two state-of-the-art methods.
For the tested CBCT images, the VD, DSC, ASSD, RMSSSD, and MSSD for the incisor were 38.16 ± 12.94 mm(3), 88.82 ± 2.14%, 0.29 ± 0.03 mm, 0.32 ± 0.08 mm, and 1.25 ± 0.58 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the canine were 49.12 ± 9.33 mm(3), 91.57 ± 0.82%, 0.27 ± 0.02 mm, 0.28 ± 0.03 mm, and 1.06 ± 0.40 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the premolar were 37.95 ± 10.13 mm(3), 92.45 ± 2.29%, 0.29 ± 0.06 mm, 0.33 ± 0.10 mm, and 1.28 ± 0.72 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the molar were 52.38 ± 17.27 mm(3), 94.12 ± 1.38%, 0.30 ± 0.08 mm, 0.35 ± 0.17 mm, and 1.52 ± 0.75 mm, respectively. The computation time of the proposed method for segmenting CBCT images of one subject was 7.25 ± 0.73 min. Compared with two other methods, the proposed method achieves significant improvement in terms of accuracy.
The presented tooth segmentation method can be used to segment tooth contours from CT images accurately and efficiently.
牙齿的三维（3D）模型为正畸诊断和治疗计划提供重要信息。牙齿分割是从计算机断层扫描（CT）图像生成3D数字模型的关键步骤。本研究的目的是开发一种从CT图像中准确、高效地分割牙齿的方法。
3D牙科CT容积图像在二维（2D）横断面上逐片进行分割。二维分割由手动初始化步骤和自动逐片分割步骤组成。在手动初始化步骤中，用户手动选择起始切片并为该切片中的每颗牙齿选择一个种子点。在自动切片分割步骤中，应用一种改进的混合水平集模型从每个切片中分割牙齿轮廓。采用牙齿轮廓传播策略自动初始化水平集函数。使用两名受试者的锥形束CT（CBCT）图像来调整参数。另外16名受试者的图像用于验证该方法的性能。采用体积重叠指标和表面距离指标对分割精度进行定量评估。体积重叠指标包括体积差（VD，mm³）和骰子相似系数（DSC，%）。表面距离指标包括平均对称表面距离（ASSD，mm）、均方根（RMS）对称表面距离（RMSSSD，mm）和最大对称表面距离（MSSD，mm）。记录计算时间以评估效率。将所提方法的性能与两种最先进的方法进行了比较。
对于测试的CBCT图像，切牙的VD、DSC、ASSD、RMSSSD和MSSD分别为38.16±12.94mm³、88.82±2.14%、0.29±0.03mm、0.32±0.08mm和1.25±0.58mm；尖牙的VD、DSC、ASSD、RMSSSD和MSSD分别为49.12±9.33mm³、91.57±0.82%、0.27±0.02mm、0.28±0.03mm和1.06±0.40mm；前磨牙的VD、DSC、ASSD、RMSSSD和MSSD分别为37.95±10.13mm³、9
