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Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy
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Published on: May 8, 2018

基于深度学习的肝脏-90选择性内部辐射疗法的自分区.

Jun Li1, Wookjin Choi1, Rani Anne1

  • 1Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA.

Technology in cancer research & treatment
|March 28, 2025
PubMed
概括

一个新的深度学习 (DL) 模型准确地为Y-90选择性内部辐射疗法 (SIRT) 细分肝脏. 这种先进的自动细分方法优于传统的以图谱为基础的方法,提高了治疗计划的可靠性.

科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 辐射疗法 辐射疗法

背景情况:

  • 准确的肝脏划分对于Y-90选择性内部辐射疗法 (SIRT) 计划至关重要.
  • 手动细分是耗时的,并且受观察者之间的变化影响.
  • 现有的自动化方法,比如基于地图集的细分,在准确度上有局限性.

研究的目的:

  • 评估基于深度学习 (DL) 的自我细分方法,用于在Y-90 SIRT中划分肝脏.
  • 为了比较DL模型的性能与医生手册划分和基于图谱的方法.

主要方法:

  • 为肝脏细分开发了一个U-Net3D深度学习架构.
  • 在SIRT患者的CT图像上测试了DL模型.
  • 绩效通过使用子相似系数 (DSC),平均距离达成一致 (MDA),体积比 (RV) 和活动比 (RA) 来评估.

主要成果:

  • 与以图谱为基础的方法相比,DL模型与手动划线达成更高的一致性 (DSC:0.94与0.83;MDA:1.8mm与7.1mm).
  • 体积和活动比率 (RV:0.99,RA:1.00) 显示了基于DL和手动细分之间的良好一致.
  • DL模型在CT图像中展示了可靠的肝脏识别和细分.
关键词:
亚特兰大 亚特兰大 亚特兰大 亚特兰大自动细分的自动细分.深度学习是一种深度学习.肝脏的划界是肝脏的划界.树脂伊特-90的树脂

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结论:

  • 基于DL的自细分方法为Y-90 SIRT提供了准确可靠的肝脏划分.
  • 这种方法在性能上超越了传统的以地图集为基础的方法.
  • 开发的DL模型适用于SIRT程序的临床应用.