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使用深度学习进行牙科修复的自动细分:探索数据增强技术.

Berrin Çelik1, Muhammed Emin Baslak2, Mehmet Zahid Genç2

  • 1Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara Yıldırım Beyazıt University, Ankara, Turkey. berrincelik@aybu.edu.tr.

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|December 9, 2024
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概括

数据增强显著改善了深度学习模型的性能,用于在全景图像中对植入物,假肢和填充物等牙结构进行细分. 最佳策略因模型和牙科类型而异.

关键词:
数据增强数据增强深度学习是一种深度学习.全景射线图 (Panoramic Radiography) 是一个全景射线图.分段化 分段化 分段化 分段化

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科学领域:

  • 人工智能在牙科中的应用
  • 医学图像分析 医学图像分析
  • 数字牙科数字牙科

背景情况:

  • 深度学习模型对于自动化牙科X光片细分至关重要.
  • 模型的性能取决于培训数据的质量和多样性.
  • 数据增强人为地扩展数据集,以改善模型通用化.

研究的目的:

  • 在全景牙科图像中自动分割植入物,假肢和填充物.
  • 评估各种数据增强技术对细分性能的影响.
  • 为了比较九种不同的深度学习细分模型的有效性.

主要方法:

  • 利用九个深度学习细分模型进行牙科图像分析.
  • 将八种不同的数据增强技术应用于训练数据集.
  • 使用交叉与联盟 (IoU) 和子系数指标评估模型性能.

主要成果:

  • 深度学习模型实现了0.62-0.82之间的IoU分数和0.75-0.9.9之间的Dice分数.
  • 数据增强使细分性能提高了高达3.37% (植入物),5.75% (假肢) 和8.75% (填充物).

结论:

  • 数据增强可以提高自动牙科图像细分的准确性.
  • 增强策略的选择取决于特定的深度学习模型和正在分析的牙结构类型.