Department of Stomatology, Beijing Friendship Hospital, Capital Medical University, 95 Yong 'an Road, Xicheng District, Beijing, 100050, China.
Department of General surgery, Beijing Huaxin Hospital, the First Affiliated Hospital of Tsinghua University, Beijing, 100016, China.
BMC Oral Health. 2023 Oct 13;23(1):752. doi: 10.1186/s12903-023-03423-y.
Accurate quantification of the root surface area (RSA) plays a decisive role in the advancement of periodontal, orthodontic, and restorative treatment modalities. In this study, we aimed to develop a dynamic threshold-based computer-aided system for segmentation and calculation of the RSA of isolated teeth on cone-beam computed tomography (CBCT) and to assess the accuracy of the measured data.
We selected 24 teeth to be extracted, including single-rooted and multi-rooted teeth, from 22 patients who required tooth extraction. In the experimental group, we scanned 24 isolated teeth using CBCT with a voxel size of 0.3 mm. We designed a computer-aided system based on a personalized dynamic threshold algorithm to automatically segment the roots of 24 isolated teeth in CBCT images and calculate the RSA. In the control group, we employed digital intraoral scanner devices to perform optical scanning on 24 isolated teeth and subsequently manually segmented the roots using 3-matic software to calculate the RSA. We used the paired t-test (P < 0.05) and Bland-Altman plots to analyze the consistency of the two measurement methods.
The results of the paired t-test showed that there was no significant difference in the RSAs obtained using the dynamic threshold method and the optical scanning image reconstruction (t = 1.005, P = 0.325 > 0.05). As per the Bland-Altman plot, the results were evenly distributed within the region of ± 1.96 standard deviations of the mean, with no increasing or decreasing trends and good consistency.
In this study, we designed a computer-aided root segmentation system based on a personalized dynamic threshold algorithm to automatically segment the roots of isolated teeth in CBCT images with a voxel size of 0.3 mm. We found that the RSA calculated using this approach was highly accurate, and a voxel of 0.3 mm in size could accurately display the surface area data in CBCT images. Overall, our findings in this study provide a foundation for future work on accurate automatic segmentation of tooth roots in full-mouth CBCT images and the computation of RSA.
准确量化根表面面积（RSA）在牙周、正畸和修复治疗方法的发展中起着决定性作用。本研究旨在开发一种基于动态阈值的计算机辅助系统，用于对锥形束 CT（CBCT）中孤立牙齿的 RSA 进行分割和计算，并评估测量数据的准确性。
我们从需要拔牙的 22 名患者中选择了 24 颗要拔出的牙齿，包括单根和多根牙齿。在实验组中，我们使用体素大小为 0.3mm 的 CBCT 对 24 颗孤立牙齿进行扫描。我们设计了一种基于个性化动态阈值算法的计算机辅助系统，自动分割 CBCT 图像中 24 颗孤立牙齿的根部并计算 RSA。在对照组中，我们使用数字口内扫描仪设备对 24 颗孤立牙齿进行光学扫描，然后使用 3-matic 软件手动分割根部以计算 RSA。我们使用配对 t 检验（P<0.05）和 Bland-Altman 图分析两种测量方法的一致性。
配对 t 检验结果表明，动态阈值法和光学扫描图像重建法获得的 RSA 无显著差异（t=1.005，P=0.325>0.05）。根据 Bland-Altman 图，结果在均值的±1.96 标准差范围内均匀分布，没有增加或减少的趋势，一致性良好。
本研究设计了一种基于个性化动态阈值算法的计算机辅助根分割系统，用于自动分割体素大小为 0.3mm 的 CBCT 图像中孤立牙齿的根部。我们发现，使用该方法计算的 RSA 非常准确，体素大小为 0.3mm 可以准确显示 CBCT 图像中的表面面积数据。总的来说，本研究的结果为未来在全口 CBCT 图像中准确自动分割牙齿根部和计算 RSA 提供了基础。
