Tooth detection and numbering in panoramic radiographs using convolutional neural networks
Tooth detection and numbering in panoramic radiographs using convolutional neural networks
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DOI：
10.1259/dmfr.20180051
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发表时间：
2019-01-01
影响因子：
3.3
通讯作者：
Bednenko, Georgiy B.
中科院分区：
文献类型：
--
作者：
Tuzoff, Dmitry, V;Tuzova, Lyudmila N.;Bednenko, Georgiy B.
目的：分析牙科X光片是日常临床实践中诊断过程的重要组成部分。专家的解释包括牙齿检测和编号。在这个项目中，提出了一种新的解决方案，基于卷积神经网络（CNN），自动执行此任务的全景radiographs.Methods：一个数据集的1352个随机选择的成人全景射线照片被用来训练系统。分析了用于牙齿检测和编号任务的基于CNN的架构。牙齿检测模块处理射线照片以限定每个牙齿的边界。它基于最先进的Faster R-CNN架构。牙齿编号模块根据FDI符号对检测到的牙齿图像进行分类。它利用经典的VGG-16 CNN和启发式算法，根据牙齿空间排列的规则来改进结果。一个单独的测试集的222幅图像被用来评估系统的性能，并将其与专家level.Results：牙齿检测任务，该系统实现了以下性能指标：0.9941的灵敏度和0.9945的精度。对于牙齿编号，其灵敏度为0.9800，特异度为0.9994。专家检测牙齿的灵敏度为0.9980，精度为0.9998。它们对牙齿编号的敏感性为0.9893，特异性为0.9997。详细的错误分析表明，开发的软件系统，使错误所造成的类似因素为expert.Conclusions：所提出的计算机辅助诊断解决方案的性能是可比的专家水平。基于这些研究结果，该方法具有实际应用的潜力和进一步的自动化牙科X光片分析的评价。计算机辅助牙齿检测和编号简化了填写数字牙科图表的过程。自动化可以帮助节省时间，提高电子牙科记录的完整性。
Objectives: Analysis of dental radiographs is an important part of the diagnostic process in daily clinical practice. Interpretation by an expert includes teeth detection and numbering. In this project, a novel solution based on convolutional neural networks (CNNs) is proposed that performs this task automatically for panoramic radiographs.Methods: A data set of 1352 randomly chosen panoramic radiographs of adults was used to train the system. The CNN-based architectures for both teeth detection and numbering tasks were analyzed. The teeth detection module processes the radiograph to define the boundaries of each tooth. It is based on the state-of-the-art Faster R-CNN architecture. The teeth numbering module classifies detected teeth images according to the FDI notation. It utilizes the classical VGG-16 CNN together with the heuristic algorithm to improve results according to the rules for spatial arrangement of teeth. A separate testing set of 222 images was used to evaluate the performance of the system and to compare it to the expert level.Results: For the teeth detection task, the system achieves the following performance metrics: a sensitivity of 0.9941 and a precision of 0.9945. For teeth numbering, its sensitivity is 0.9800 and specificity is 0.9994. Experts detect teeth with a sensitivity of 0.9980 and a precision of 0.9998. Their sensitivity for tooth numbering is 0.9893 and specificity is 0.9997. The detailed error analysis showed that the developed software system makes errors caused by similar factors as those for experts.Conclusions: The performance of the proposed computer-aided diagnosis solution is comparable to the level of experts. Based on these findings, the method has the potential for practical application and further evaluation for automated dental radiograph analysis. Computer-aided teeth detection and numbering simplifies the process of filling out digital dental charts. Automation could help to save time and improve the completeness of electronic dental records.