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Individual tooth detection and identification from dental panoramic X-ray images via point-wise localization and distance regularization
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2020-11-21 , DOI: 10.1016/j.artmed.2020.101996
Minyoung Chung 1 , Jusang Lee 1 , Sanguk Park 1 , Minkyung Lee 1 , Chae Eun Lee 1 , Jeongjin Lee 2 , Yeong-Gil Shin 1
Affiliation  

Dental panoramic X-ray imaging is a popular diagnostic method owing to its very small dose of radiation. For an automated computer-aided diagnosis system in dental clinics, automatic detection and identification of individual teeth from panoramic X-ray images are critical prerequisites. In this study, we propose a point-wise tooth localization neural network by introducing a spatial distance regularization loss. The proposed network initially performs center point regression for all the anatomical teeth (i.e., 32 points), which automatically identifies each tooth. A novel distance regularization penalty is employed on the 32 points by considering L2 regularization loss of Laplacian on spatial distances. Subsequently, teeth boxes are individually localized using a multitask neural network on a patch basis. A multitask offset training is employed on the final output to improve the localization accuracy. Our method successfully localizes not only the existing teeth but also missing teeth; consequently, highly accurate detection and identification are achieved. The experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches by increasing the average precision of teeth detection by 15.71 % compared to the best performing method. The accuracy of identification achieved a precision of 0.997 and recall value of 0.972. Moreover, the proposed network does not require any additional identification algorithm owing to the preceding regression of the fixed 32 points regardless of the existence of the teeth.



中文翻译:

通过逐点定位和距离正则化从牙科全景 X 射线图像中检测和识别个体牙齿

由于其辐射剂量非常小,牙科全景 X 射线成像是一种流行的诊断方法。对于牙科诊所的自动计算机辅助诊断系统,从全景 X 射线图像中自动检测和识别单个牙齿是关键的先决条件。在这项研究中,我们通过引入空间距离正则化损失提出了一种逐点牙齿定位神经网络。提议的网络最初对所有解剖牙齿(即 32 个点)执行中心点回归,自动识别每颗牙齿。通过考虑对 32 个点采用新的距离正则化惩罚2拉普拉斯算子在空间距离上的正则化损失。随后,在补丁的基础上使用多任务神经网络单独定位牙齿盒。在最终输出上采用多任务偏移训练以提高定位精度。我们的方法不仅成功地定位了现有的牙齿,还成功地定位了缺失的牙齿;因此,实现了高度准确的检测和识别。实验结果表明,与性能最佳的方法相比,所提出的算法将牙齿检测的平均精度提高了 15.71%,从而优于最先进的方法。识别准确率达到0.997,召回值为0.972。而且,

更新日期:2020-12-05
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