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头部和部总瘤体积自动细分使用PocketNet

Awj Twam1, Adrian Celaya1, Evan Lim2

  • 1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings
|June 11, 2025
PubMed
概括

团队口袋使用了PocketNet,一个轻量级的卷积神经网络 (CNN),用于自动化头癌 (HNC) 瘤细分. 这种人工智能方法在MRI图像中细分初级和节点瘤方面取得了有希望的结果,有助于治疗规划.

关键词:
自动化细分系统 自动化细分系统卷积神经网络是一种卷积神经网络.总瘤体积 总瘤体积头部和部癌症 头部和部癌症

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科学领域:

  • 医学成像和人工智能 医学成像和人工智能
  • 瘤学和辐射治疗.
  • 计算解剖学的计算解剖学

背景情况:

  • 头癌 (HNC) 构成一个重大的全球健康挑战,需要精确的瘤体积划分,以进行有效的治疗计划.
  • 在MRI图像中手动细分瘤总体积 (GTV) 是耗时的,劳动密集的,并且受观察者之间的变化影响.
  • 自动化细分技术,特别是采用深度学习的技术,对于提高HNC处理的效率和一致性至关重要.

研究的目的:

  • 评估PocketNet的有效性,一个轻量级的卷积神经网络 (CNN),用于自动细分头癌 (HNC) 中的初级 (GTVp) 和结节 (GTVn) 总瘤体积,从放射治疗前的MRI图像.
  • 在HNTS-MRG 2024大挑战的背景下评估PocketNet的性能,任务1.
  • 展示自动化细分的潜力,以提高HNC处理工作流程.

主要方法:

  • 应用PocketNet,一种轻量级的CNN架构,用于对MR图像中的GTVp和GTVn进行细分.
  • 参与HNTS-MRG 2024大挑战任务1,专注于放射治疗前的MR数据.
  • 使用子索伦森系数 (DSCagg) 进行绩效评估的定量评估.

主要成果:

  • 在 PocketNet 上,GTVn 的 Dice Sorensen 系数 (DSCagg) 总和为 0.808,GTVp 的则为 0.732.
  • 两种瘤类型的整体平均性能为0.77.
  • 这些结果表明,HNC的自动瘤细分表现强.

结论:

  • 作为一个高效和准确的工具,PocketNet在MR引导的HNC干预中展示了作为自动GTV细分的高效和准确工具的巨大潜力.
  • 开发的方法显示了将其整合到临床工作流程中的希望,这可能有助于改善治疗规划和交付.
  • 对PocketNet架构和培训策略的进一步优化可能会为HNC带来更高的细分精度.