基于单步神经网络的牙根与下颌管关系自动检测

为了提高曲面体层片中下颌阻生智齿牙根与下颌管位置关系的识别精度和效率，提出一种基于深度卷积神经网络的自动检测方法。该方法将下颌阻生智齿牙根与下颌管位置关系的自动检测视为回归任务与分类任务的结合，以YOLOv5网络为框架构建可同时完成分类和定位任务的深度卷积神经网络，将对应锥形束CT图像中获取的空间位置关系信息作为分类金标准，训练其学习曲面体层片图像特征与接触下颌管的智齿牙根之间的非线性关系。将新获得的曲面体层片输入到训练好的网络模型后，即可获得该曲面体层片下颌阻生智齿牙根与下颌管相互接触的概率值，同时预测出存在牙根与下颌管相互接触情况的区域。实验结果表明，本文方法能准确地判断出下颌阻生智齿牙根与下颌管是否接触，并能预测出存在牙根与下颌管相互接触情况的区域；与人工判读和其他方法相比，能获得更准确的检测结果。 

Automatic Identification of Relationship between Tooth Root and Mandibular Canal Based on One Step Deep Neural Network

To improve the accuracy and efficiency of identifying the relationship between the root of the impacted mandibular third molar (M3M) and the mandibular canal in panoramic radiographs, we proposed an automatic method based on a deep convolutional neural network. This method treats the automatic identification of the relationship between the root of the M3M and the mandibular canal as a combination of regression and classification tasks. It uses the YOLOv5 (You Only Look Once) network as a framework for constructing a deep convolutional neural network that can accomplish detection and classification tasks simultaneously. This network, which takes the spatial relationship information extracted from the corresponding cone-beam CT images as the ground-truth, was trained to learn the nonlinear relationship between image features and the root of the M3M contacting the mandibular canal. When inputting a newly acquired panoramic radiograph into the trained network, the network will output the probability value for the root of the M3M contacting the mandibular canal. In the meantime, the region that includes the root of the M3M contacting the mandibular canal can be predicted. The experimental results show that the proposed method can provide an accurate judgment of whether the roots of impacted mandibular wisdom teeth in the panoramic radiographs are in contact with the mandibular canal and the location of regions in which the roots of the M3M are in contact with the mandibular canals; compared to manual diagnosis and the other methods, the proposed method can obtain more accurate results.