基于局部注意力机制的三维牙齿模型分割网络

张凌明; 赵悦; 李鹏程; 刘洋; 高陈强

doi:10.12263/DZXB.20201338

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基于局部注意力机制的三维牙齿模型分割网络

学术论文 | 更新时间：2025-12-11

- 基于局部注意力机制的三维牙齿模型分割网络
- Dental Model Segmentation Network Based on Local Attention Mechanism
- 电子学报 2022年50卷第3期页码：681-690
- 作者机构：
  
  1.重庆邮电大学通信与信息工程学院,重庆 400065
  2.信号与信息处理重庆市重点实验室,重庆, 400065
  3.重庆医科大学附属口腔医院,重庆, 400060
- 作者简介：
  
  [ "张凌明男，1997年9月生于重庆，现为重庆邮电大学通信与信息工程学院硕士研究生，主要研究方向为计算机视觉、医学图像处理.E-mail:zhanglingming1997@qq.com" ]
  [ "赵悦女，1988年10月生于吉林.2017年于长春理工大学获得学士学位，2017年获得吉林大学博士学位.现任重庆邮电大学通信与信息工程学院讲师.主要研究方向包括图像处理和分析、模式识别、医学图像处理. E-mail:zhaoyue@cqupt.edu.cn" ]
  [ "李鹏程男，1995年12月生于重庆.现为重庆邮电大学通信与信息工程学院博士研究生.主要研究方向为医学图像处理与分析，计算机视觉和机器学习.E-mail:lipengchengme@163.com" ]
  [ "刘洋男，1987年6月生于重庆，2014年于北京大学口腔医学院获得口腔医学博士，2017年获得北京大学口腔医学院正畸学博士.现任重庆医科大学附属口腔医院正畸科医师.主要研究方向，正畸牙移动的生物学机制，牙齿图像处理及应用.E-mail:yangliu@cqmu.ecu.cn" ]
  [ "高陈强(通讯作者) 男，1981年8月生于重庆.于华中科技大学获得博士学位.现任重庆邮电大学通信与信息工程学院一名教授和博士生导师.主要研究方向包括红外图像分析、目标检测与识别、行为识别." ]
- 基金信息：
  
  国家自然科学基金(61571071;61906025);重庆市自然科学基金项目(cstc2018jcyjAX0227;cstc2020jcyj-msxmX0835);重庆市教委科学技术研究项目(KJQN201900607;KJQN202000647)
- DOI：10.12263/DZXB.20201338
  中图分类号： TP753
- 收稿：2020-11-26，
  
  修回：2021-06-02，
  
  纸质出版：2022-03-25
- 稿件说明：
移动端阅览
张凌明,赵悦,李鹏程等.基于局部注意力机制的三维牙齿模型分割网络[J].电子学报,2022,50(03):681-690.

ZHANG Ling-ming,ZHAO Yue,LI Peng-cheng,et al.Dental Model Segmentation Network Based on Local Attention Mechanism[J].ACTA ELECTRONICA SINICA,2022,50(03):681-690.
张凌明,赵悦,李鹏程等.基于局部注意力机制的三维牙齿模型分割网络[J].电子学报,2022,50(03):681-690. DOI： 10.12263/DZXB.20201338.

ZHANG Ling-ming,ZHAO Yue,LI Peng-cheng,et al.Dental Model Segmentation Network Based on Local Attention Mechanism[J].ACTA ELECTRONICA SINICA,2022,50(03):681-690. DOI： 10.12263/DZXB.20201338.

摘要

从三维牙齿模型中准确分割出牙齿部分是正畸计算机辅助诊疗的基础.由于现有的三维模型分割网络对局部特征建模方式相对简单，这些方法无法有效提取牙齿边缘区域更细节的局部特征信息，进而导致这些区域出现牙齿多分、漏分等情况.本文提出一种基于局部注意力机制的三维牙齿模型分割网络以提高牙齿边缘区域的分割性能.首先，对原始牙齿模型中的三维网格数据进行多尺度的局部空间区域构建.其次，根据每个局部区域内的网格空间分布和网格特征差异进行注意力权重的学习.最后，基于学习到的网格权重进行局部特征聚合，以使得网络能自适应地去关注各个局部区域内更具有表达性网格特征.在临床数据集上的实验结果表明，相对于现有方法，本文网络的分割结果在牙齿边界区域更加准确光滑.

Abstract

Accurate tooth segmentation from 3D dental model is the basis of computer-aided-design (CAD) for orthodontic treatment. Due to the relatively coarse modeling of local feature

existing 3D shape segmentation networks cannot effectively extract more detailed local feature on teeth boundaries. This issue will further result in over-segmentation or under-segmentation on boundaries. In this paper

a 3D dental model segmentation network based on local attention mechanism is proposed to improve segmentation performance on teeth boundaries. Firstly

multi-scale local spaces are constructed for 3D mesh data of raw dental model. Secondly

attention weights are learned based on the spatial distribution and feature differences of meshes for each local space. Finally

a local feature aggregation is applied based on learned attention weights of meshes to make the network automatically focus on more representive mesh features in each local space. The proposed network is evaluated on a real-patient datasets

and the experimental results show that our network can more clearly and accurately segment teeth boundaries when compared with existing methods.

关键词

Keywords

references

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