基于深度学习的牙齿嵌塞自动判别方法

doi:10.19678/j.issn.1000-3428.0061934

计算机工程 ›› 2022, Vol. 48 ›› Issue (4): 307-313. doi: 10.19678/j.issn.1000-3428.0061934

基于深度学习的牙齿嵌塞自动判别方法

王志江¹, 秦品乐¹, 柴锐¹, 武峰², 程一彤², 史玥²

1. 中北大学大数据学院山西省生物医学成像与影像大数据重点实验室, 太原 030051;
2. 山西医科大学口腔医院修复科, 太原 030012

收稿日期:2021-06-16 修回日期:2021-08-09 发布日期:2022-04-14
作者简介:王志江(1996—),男,硕士研究生,主研方向为三维点云、深度学习;秦品乐,教授;柴锐(通信作者),博士;武峰,教授;程一彤、史玥,硕士研究生。
基金资助:
山西省重点研发计划项目（201903D321120）；山西省高等学校科技创新项目（2019L0533）。

Automatic Identification Method of Tooth Impaction Based on Deep Learning

WANG Zhijiang¹, QIN Pinle¹, CHAI Rui¹, WU Feng², CHENG Yitong², SHI Yue²

1. Shanxi Provincial Key Laboratory of Biomedical Imaging and Imaging Big Data, College of Big Data, North University of China, Taiyuan 030051, China;
2. Department of Prosthodontics, Stomatological Hospital of Shanxi Medical University, Taiyuan 030012, China

Received:2021-06-16 Revised:2021-08-09 Published:2022-04-14

摘要/Abstract

摘要： 食物嵌塞是口腔常见病征，容易引发局部牙龈红肿、溢脓、龋齿等口腔问题，给患者带来极大的痛苦和不便。目前临床上难以自动筛查嵌塞牙齿，且传统的锥形束CT重建方法的准确度及精度均有待提高。提出一种牙齿嵌塞自动化判断的方法，对牙齿模型进行单个牙体的精准分割，在U-Net网络的基础上使用KPConv卷积核代替二维卷积核来构建分割网络，并使用图割方法优化分割结果。同时，采用平面拟合的方法将分割后的牙齿模型投影到水平和竖直平面上，在平面上求出牙齿嵌塞特征，并利用支持向量机根据所求特征对牙齿的嵌塞情况进行判断。通过充分利用样本模型的几何结构信息，设计简化牙齿模型的几何采样及包含牙齿几何结构约束的图割方法提高网络模型的运算时间及精度。实验结果表明，该方法对牙齿模型的分割准确率为92%，对牙齿嵌塞的判断正确率为81%，能够为医生提供辅助诊断。

关键词: 深度学习, 点云分割, 牙齿分割, 食物嵌塞, 支持向量机

Abstract: Food impaction is a common oral symptom, which easily causes oral problems, such as local gingival redness, pus overflow, and dental caries, bringing great pain and inconvenience to patients.At present, automatically screening impacted teeth in clinics is difficult, and the accuracy of the traditional cone beam CT reconstruction method must improve.An automatic judgment method of tooth impaction is proposed to accurately segment a single tooth in the tooth model.Based on the U-NET network, a KPConv convolution kernel was used, instead of a two-dimensional convolution kernel, to construct the segmentation network, and the graph cut method was used to optimize the segmentation results.Meanwhile, the segmentation model after the embedded teeth plug features according to the relevant judgment of whether to plug embedded teeth using the plane projection method from the horizontal to the vertical plane, the plane and the embedded teeth characteristics, and the support vector machine asking for the teeth embedded plug profile of judgment.The geometric sampling of the simplified tooth model and graph cutting method containing the constraints of the tooth geometric structure were designed by fully using the geometric structure information of the sample model, improving the operation time and accuracy of the network model.The results show that the segmentation accuracy of the tooth model can reach 92%, and the judgment accuracy of tooth impaction can reach 81%, which can provide doctors with auxiliary diagnoses.

Key words: deep learning, point cloud segmentation, tooth segmentation, food embedded plug, Support Vector Machine (SVM)

中图分类号:

TP391

王志江, 秦品乐, 柴锐, 武峰, 程一彤, 史玥. 基于深度学习的牙齿嵌塞自动判别方法[J]. 计算机工程, 2022, 48(4): 307-313.

WANG Zhijiang, QIN Pinle, CHAI Rui, WU Feng, CHENG Yitong, SHI Yue. Automatic Identification Method of Tooth Impaction Based on Deep Learning[J]. Computer Engineering, 2022, 48(4): 307-313.

http://www.ecice06.com/CN/Y2022/V48/I4/307

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