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基于X射线图的增强全景牙分割和识别,使用基于注意门的编码解码网络.

Salih Taha Alperen Özçelik1, Hüseyin Üzen2, Abdulkadir Şengür3

  • 1Department of Electrical and Electronics Engineering, Faculty of Engineering, Bingöl University, Bingöl 12000, Turkey.

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|December 17, 2024
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概括
此摘要是机器生成的。

这项研究引入了一种人工智能驱动的深度学习模型,用于在牙科X射线中精确地对牙进行细分,从而提高诊断精度. 基于Squeeze和Excitation Inception Block的编码解码器网络在识别牙区域方面取得了高性能.

关键词:
注意力门的注意力门.编码器 解码器挤压和兴奋的激发.牙标签的使用方法牙细分的细分是指牙的细分.

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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
  • 对于牙科应用的深度学习

背景情况:

  • 牙疾病影响全球数十亿人,需要早期诊断才能有效治疗.
  • 手动的牙细分和编号容易出现错误,阻碍了精确的牙科疾病诊断.
  • 人工智能 (AI),特别是深度学习,为牙科图像处理提供了自动化,快速和准确的解决方案.

研究的目的:

  • 开发和评估一种基于人工智能的方法,用于在全景X射线图像中实现自动牙细分.
  • 提高牙识别的准确性和效率,以实现更好的牙科诊断.
  • 为了解决临床环境中手动牙细分的局限性.

主要方法:

  • 提出了基于挤压和激发开始区块的编码解码器 (SE-IB-ED) 网络,用于牙细分.
  • 使用InceptionV3进行编码和自定义解码器,具有注意力机制,用于功能集成.
  • 用FDI系统注释了313张全景放射图的数据集,使用SAM进行自动标记和手动校正.

主要成果:

  • 在SE-IB-ED网络实现了92.65%的F1得分,86.38%的mIoU和92.84%的准确性.
  • 与最先进的模型相比,在牙细分方面表现出卓越的性能.
  • 在细分牙区域中实现了高精度 (92.49%) 和回忆 (99.92%) .

结论:

  • 拟议的SE-IB-ED网络显示出在全景X射线中精确细分牙和背景的巨大潜力.
  • 自动化细分可以提高牙科诊断工作流程和治疗规划.
  • 深度学习模型为复杂的牙科图像分析任务提供了强大的解决方案.