Morishita Takumi, Muramatsu Chisako, Seino Yuta, Takahashi Ryo, Hayashi Tatsuro, Nishiyama Wataru, Zhou Xiangrong, Hara Takeshi, Katsumata Akitoshi, Fujita Hiroshi
Gifu University, Graduate School of Natural Science and Technology, Department of Intelligence Science and Engineering, Gifu, Japan.
Shiga University, Faculty of Data Science, Hikone, Japan.
J Med Imaging (Bellingham). 2022 May;9(3):034503. doi: 10.1117/1.JMI.9.3.034503. Epub 2022 Jun 22.
The purpose of our study was to analyze dental panoramic radiographs and contribute to dentists' diagnosis by automatically extracting the information necessary for reading them. As the initial step, we detected teeth and classified their tooth types in this study. We propose single-shot multibox detector (SSD) networks with a side branch for 1-class detection without distinguishing the tooth type and for 16-class detection (i.e., the central incisor, lateral incisor, canine, first premolar, second premolar, first molar, second molar, and third molar, distinguished by the upper and lower jaws). In addition, post-processing was conducted to integrate the results of the two networks and categorize them into 32 classes, differentiating between the left and right teeth. The proposed method was applied to 950 dental panoramic radiographs obtained at multiple facilities, including a university hospital and dental clinics. The recognition performance of the SSD with a side branch was better than that of the original SSD. In addition, the detection rate was improved by the integration process. As a result, the detection rate was 99.03%, the number of false detections was 0.29 per image, and the classification rate was 96.79% for 32 tooth types. We propose a method for tooth recognition using object detection and post-processing. The results show the effectiveness of network branching on the recognition performance and the usefulness of post-processing for neural network output.
我们研究的目的是分析牙科全景X光片，并通过自动提取阅读所需信息来辅助牙医进行诊断。作为第一步，我们在本研究中检测牙齿并对其类型进行分类。我们提出了带有侧分支的单阶段多框检测器（SSD）网络，用于不区分牙齿类型的1类检测以及16类检测（即中切牙、侧切牙、尖牙、第一前磨牙、第二前磨牙、第一磨牙、第二磨牙和第三磨牙，按上下颌区分）。此外，进行了后处理以整合两个网络的结果，并将它们分类为32类，区分左右牙齿。所提出的方法应用于在包括大学医院和牙科诊所在内的多个机构获取的950张牙科全景X光片。带有侧分支的SSD的识别性能优于原始SSD。此外，通过整合过程提高了检测率。结果，检测率为99.03%，每张图像的误检数为0.29，32种牙齿类型的分类率为96.79%。我们提出了一种使用目标检测和后处理的牙齿识别方法。结果表明网络分支对识别性能的有效性以及神经网络输出后处理的有用性。
