中国口腔种植学杂志 ›› 2024, Vol. 29 ›› Issue (1): 82-86.DOI: 10.12337/zgkqzzxzz.2024.02.014

• 综述 • 上一篇    下一篇

基于CBCT的深度学习辅助解剖结构分割在口腔种植中的应用

高乾程1, 李新东2, 曹明国1, 刘云峰3   

  1. 1丽水学院医学院,丽水 323020,浙江;
    2肇庆学院计算机科学与软件学院,肇庆 526061,广东;
    3浙江工业大学机械工程学院,杭州 310023
  • 收稿日期:2023-07-17 出版日期:2024-02-29 发布日期:2024-03-07
  • 通讯作者: 曹明国,Email:cmg@lsu.edu.cn,电话:0578-2273763;刘云峰,Email:liuyf76@126.com,电话:0571-85290825
  • 作者简介:高乾程 口腔医学专业本科在读,研究方向:口腔临床医学;曹明国 副教授、硕士生导师,研究方向:口腔种植修复;刘云峰 博士、三级教授、博士生导师,研究方向:数字化口腔医学
  • 基金资助:
    浙江省大学生创新创业训练计划(S202110352018)

Application of deep learning-assisted anatomical structure segmentation based on CBCT in implant dentistry

Gao Qiancheng1, Li Xindong2, Cao Mingguo1, Liu Yunfeng3   

  1. 1School of medicine,Lishui University, Lishui 323020, Zhejiang, China;
    2School of Computer Science and Software,Zhaoqing University, Zhaoqing 526061, Guangdong, China;
    3College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, Zhejiang, China
  • Received:2023-07-17 Online:2024-02-29 Published:2024-03-07
  • Contact: Cao Mingguo, Email: cmg@lsu.edu.cn, Tel: 0086-578-2273763; Liu Yunfeng, Email: liuyf76@126.com, Tel: 0086-571-85290825
  • Supported by:
    Zhejiang Student's Innovation and Entrepreneurship Training Program (S202110352018)

摘要: 应用深度学习进行口腔解剖结构分割相比手动分割及传统算法分割可高效获得精准、一致性良好的分割结果。该方法可以快速获得术区解剖结构信息,进行口腔种植手术及口腔修复方案的设计。本文拟对基于锥形束计算机体层成像的深度学习在口腔种植领域解剖结构分割方面的研究进展做一综述。

关键词: 口腔种植, 深度学习, 锥形束计算机体层成像, 解剖结构, 分割

Abstract: Compared with manual segmentation and traditional algorithm segmentation, the application of deep learning for oral anatomical structure segmentation can obtain accurate and consistent segmentation results efficiently. This approach can quickly obtain the anatomical structure information of the surgical areas to design implant surgery and restoration. This review provides an overview of the progress in CBCT-based deep learning for anatomical structure segmentation in the field of implant dentistry.

Key words: Implant dentistry, Deep learning, Cone beam computed tomography, Anatomical structure, Segmentation