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在放射治疗中使用深度学习进行自动细分.

Lars Johannes Isaksson1,2, Paul Summers3, Federico Mastroleo1,4

  • 1Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy.

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概括
此摘要是机器生成的。

本综述分析了807项用于放射治疗中的自动细分的深度学习研究,为医疗图像细分和放射治疗的未来研究提供了实际指南.

关键词:
人工智能的人工智能是人工智能.人工神经网络的人工神经网络这是一个自动化的自动化.深度学习是一种深度学习.辐射疗法 辐射疗法细分化 细分化的细分化

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科学领域:

  • 辐射疗法 辐射疗法
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 深度学习越来越多地用于放射治疗中的自动细分.
  • 现有的研究涵盖了各种癌症部位,成像方式 (CT,MRI,PET) 和细分技术.
  • 需要对当前研究进行全面的概述,以确定趋势和研究差距.

研究的目的:

  • 在放射治疗中正式审查和分析基于深度学习的自动细分的景观.
  • 为了发现807篇发表论文的共同点,趋势和方法.
  • 为该领域的研究人员提供可操作的见解和实用指南.

主要方法:

  • 对807篇发表的关于深度学习的论文进行系统审查,用于放射治疗中的自动细分.
  • 收集和分析有关癌症部位,图像类型和细分方法的关键统计数据.
  • 使用ChatGPT进行信息凝聚和分析.

主要成果:

  • 在放射治疗中的深度学习细分研究中确定了共同点和趋势.
  • 需要进一步研究和调查的突出领域.
  • 为进行有效的细分研究提供了实际指导方针.

结论:

  • 该综述提供了利用深度学习在放射治疗中自动细分现状的结构化概述.
  • 提供了可操作的见解和指导方针,以改善研究实践,并为未来的研究提供信息.
  • 这项工作是研究人员在医学图像细分的竞争领域进行导航的宝贵资源.