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医学成像中的基于深度学习的牙细分方法:一篇综述

Xiaokang Chen1, Nan Ma2,3, Tongkai Xu4

  • 1Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
|February 5, 2024
PubMed
概括

像CNN和变压器这样的深度学习模型正在推进牙细分用于牙科分析. 本综述涵盖了牙科全景放射,CBCT和口腔内扫描的方法,强调了未来的研究方向.

关键词:
3D点云是一个3D点云.深度学习是一种深度学习.卷积神经网络是一种卷积神经网络.牙图像 牙图像 牙图像 牙图像牙细分的细分是指牙的细分.

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科学领域:

  • 人工智能的人工智能
  • 医疗成像医学成像
  • 牙科 牙科是指牙科的专业.

背景情况:

  • 深度学习,特别是卷积神经网络 (CNN) 和变压器,已经显著推进了医疗图像分析.
  • 牙细分对于临床牙科评估,病理诊断和手术规划至关重要.
  • 现有的深度学习模型,如U-Net,Mask R-CNN和SETR,为牙细分提供了基础框架.

研究的目的:

  • 审查各种牙科成像模式的牙细分的深度学习方法.
  • 讨论提高性能的技术,并确定当前牙细分研究的局限性.
  • 为未来的研究提供见解,并促进自动化牙细分的更广泛的临床采用.

主要方法:

  • 审查包括CNN和变压器在内的深度学习技术,用于牙细分.
  • 对牙科全景放射 (DPR),圆束计算机断层扫描 (CBCT) 图像和口腔内扫描 (IOS) 模型应用的模型分析.
  • 讨论增强和优化模块,以提高细分性能.

主要成果:

  • 深度学习模型在导出牙特征图表方面取得了显著进展.
  • 已经提出了各种架构和增强模块,以提高细分精度.
  • 数据注释和模型概括等挑战仍然存在,影响了广泛的临床使用.

结论:

  • 深度学习为牙科中自动化牙细分提供了强大的工具.
  • 需要进一步的研究来解决目前的局限性,并提高模型的稳定性.
  • 改善牙细分可以显著帮助临床决策和患者护理.