Objective: To develop an automated landmark location system applicable to the case of landmark missing. Methods: Four and eighty-one lateral cephalograms, which contained 240 males and 241 females, with an average age of (24.5±5.6) years, taken from January 2015 to January 2021 in the Department of Orthodontics, Capital Medical University School of Stomatology, and met the inclusion criteria were collected. Five postgraduate orthodontic students were the annotators to manually locate 61 possible landmarks in 481 lateral cephalograms. Two assistant professors in the department as reviewers performed calibration. Two professors as arbitrators, made final decision. Data sets were established (341 were used as training set, 40 as validation set, and 100 as test set). In this paper, an automatic landmarks identification and location model based on convolutional neural networks (CNN), CephaNET, was developed. The model was trained by feeding the original image into the feature extraction module and convolutional pose machine (CPM) module to locate landmarks with high accuracy using deep supervision. Training set was enhanced to 1 684 images by histogram equalization, cropping, and adjustment of brightness. The model was trained to compare the Gaussian heat maps output from the network with the set threshold to identify landmark missing cases. Test set of 100 lateral cephalograms was used to test the accuracy of the model. The evaluation criteria used were success detection rate of missing landmark, mean radial error (MRE) and success detection rate (SDR) in the range of 2.0, 2.5, 3.0, 3.5 and 4.0 mm. Results: The model identified and located 61 commonly used landmarks in 0.13 seconds on average. It had an average accuracy of 93.5% in identifying missing landmarks. The MRE of our testing set was (1.19±0.91) mm. SDR of 2.0, 2.5, 3.0, 3.5 and 4.0 mm were 85.4%, 90.2%, 93.5%, 95.4%, 97.0% respectively. Conclusions: The model proposed in this paper could adapt to the absence of landmark in lateral cephalograms and locate 61 commonly used landmarks with high accuracy to meet the requirements of different cephalometric analysis methods.
目的： 基于卷积神经网络开发头影测量自动定点模型，以期为智能识别头颅侧位X线片上缺失的标志点并高精度实现头影测量自动定点提供参考。 方法： 收集2015年1月至2021年1月于首都医科大学口腔医学院正畸科就诊的错(牙合)畸形患者的481张头颅侧位X线片，其中男性240张，女性241张，年龄（24.5±5.6）岁。以5名正畸专业研究生为标注人员，手动定位481张头颅侧位X线片中的61个头影测量标志点；以2名正畸主治医师为审核人员，进行定点校准；以2名正畸主任医师为仲裁人员，作出最终决策。建立数据集：其中341张作为训练集，40张作为验证集，100张作为测试集。研发一种基于卷积神经网络（CNN）的自动定点模型CephaNET，通过将原始图像输入特征提取模块和卷积姿态机模块，使用深监督技术，训练CephaNET模型高精度定位标志点；将训练集通过直方图均衡化、裁剪、调节亮度，增强为1 684张，训练模型对比网络输出的高斯热力图与设置的阈值，以识别标志点缺失情况。测试集100张头颅侧位X线片用于检测模型的准确性。评价标准使用缺失标志点［颈点（Cv点）和Bolton点］识别成功率、平均径向误差（MRE）以及2.0、2.5、3.0、3.5和4.0 mm范围内成功检测率（SDR）。 结果： 针对1张头影测量X线片，CephaNET模型平均在0.13 s内识别并定位61个常用标志点；Cv点识别成功率为92.7%（38/41），Bolton点识别成功率为94.3%（50/53），平均为93.5%。测试集MRE为（1.19±0.91）mm，2.0、2.5、3.0、3.5和4.0 mm范围内SDR分别为85.4%、90.2%、93.5%、95.4%、97.0%。 结论： 本项研究研发的自动定点模型可适应头颅侧位X线片标志点缺失的情况，并高精度定位61个常用的头影测量标志点，满足不同头影测量分析要求。.