基于单步神经网络的牙根与下颌管关系自动检测  

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

作　　者：周炎锜 戴修斌[1] 王东苗[2] 朱书进 冒添逸 ZHOU Yanqi;DAI Xiubin;WANG Dongmiao;ZHU Shujin;MAO Tianyi(School of Geographic and Biologic Information,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;School of Stomatology,Nanjing Medical University,Nanjing 211166,China)

机构地区：[1]南京邮电大学地理与生物信息学院,南京210023 [2]南京医科大学附属口腔医学院,南京211166

出　　处：《CT理论与应用研究（中英文）》2023年第2期198-208,共11页Computerized Tomography Theory and Applications

基　　金：江苏省自然科学基金(基于多尺度细粒度网络和二值自编码模型的病理图像快速检索研究(BK20200745));江苏省卫生健康委面上项目(基于深度学习模型精准评估下颌智齿拔除术并发下牙槽神经损伤的临床研究(M2020021));江苏省高等学校自然科学研究项目(基于动态变尺度栈式二值自编码的病理图像实时检索研究(20KJB510022))。

摘　　要：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。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 conebeam 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.

关 键 词：深度卷积神经网络 曲面体层片 下颌阻生智齿 位置关系 

分 类 号：O242[理学—计算数学] R814[理学—数学]