Department of Orthodontics, Graduate School, Kyung Hee University, Seoul 02447, Korea.
Department of Orthodontics, Peking University School of Stomatology, Beijing 100081, China.
Sensors (Basel). 2021 Jan 12;21(2):505. doi: 10.3390/s21020505.
This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested multi-stage CNNs with 430 lateral and 430 MIP lateral cephalograms synthesized by cone-beam computed tomography (CBCT) to make a combination dataset. Fifteen landmarks were manually and respectively identified by experienced examiner, at the preprocessing phase. The intra-examiner reliability was high (ICC = 0.99) in manual identification. The results of prediction of the system for average mean radial error (MRE) and standard deviation (SD) were 1.03 mm and 1.29 mm, respectively. In conclusion, different types of image data might be the one of factors that affect the prediction accuracy of a fully-automated landmark identification system, based on multi-stage CNNs.
本研究旨在开发和验证一种基于多阶段卷积神经网络（CNN）架构的全自动头影测量标志点识别系统，使用组合数据集。在这项研究中，我们使用锥形束计算机断层扫描（CBCT）合成的 430 个侧位和 430 个 MIP 侧位头影测量片训练和测试了多阶段 CNN，以构建组合数据集。在预处理阶段，由经验丰富的检查者手动分别识别了 15 个标志点。手动识别的组内可靠性很高（ICC=0.99）。系统预测的平均径向误差（MRE）和标准偏差（SD）的结果分别为 1.03 毫米和 1.29 毫米。总之，不同类型的图像数据可能是影响基于多阶段 CNN 的全自动标志点识别系统预测准确性的因素之一。
