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基于深度学习的肺部图像注册:一篇综述

Hanguang Xiao1, Xufeng Xue1, Mi Zhu1

  • 1College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.

Computers in biology and medicine
|September 11, 2023
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概括
此摘要是机器生成的。

本综述调查了肺部图像注册的深度学习 (DL) 方法,解决了软组织运动等挑战. 它对DL方法进行了分类,并分析了它们对精确肺部成像的有效性.

关键词:
传统的图像注册方式.深度学习是一种深度学习.可变形的注册表可以变形.肺部图像注册 肺部图像注册

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 放射学 放射学是一门学科.

背景情况:

  • 肺部图像注册对于临床应用至关重要,但被软组织运动所挑战.
  • 对于其他感兴趣区域 (ROI) 现有的深度学习 (DL) 方法不足以进行肺部注册.

研究的目的:

  • 提供基于DL的肺图像记录方法的全面审查.
  • 将DL方法分类并分析它们的贡献和局限性.

主要方法:

  • 对传统和基于DL的肺部图像记录技术的调查.
  • 根据监督类型对DL方法的分类:完全监督,弱监督和无监督.
  • 在引用论文中的评估指标和损失函数的统计分析.

主要成果:

  • DL方法有前途,但肺部注册的多功能框架缺乏.
  • 分析各种DL方法的挑战,局限性和贡献.
  • 关于肺部图像注册的公开数据集的摘要.

结论:

  • 深度学习为肺部图像注册提供了巨大的潜力.
  • 需要进一步的研究来克服目前的局限性,并建立标准化的框架.
  • 未来的趋势表明DL在肺成像中的准确性提高和更广泛的临床适用性.