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使用卷积网络量化腹部脂肪.

Daniel Schneider1,2, Tobias Eggebrecht1,3, Anna Linder1

  • 1Department of Diagnostic and Interventional Radiology, Leipzig University Hospital, Liebigstr. 20, Haus 4, 04103, Leipzig, Germany.

European radiology
|July 12, 2023
PubMed
概括

使用深度学习完全卷积网络 (FCN) 自动化脂肪组织量化在肥胖患者中表现出高准确性和可靠性. 这种先进的方法提供了一个有希望的替代手动分析的身体组成评估.

关键词:
脂肪组织的脂肪组织.深度学习是一种深度学习.图像处理,计算机辅助图像处理.磁共振成像技术 磁共振成像技术肥胖问题 肥胖问题

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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:

  • 准确的脂肪组织量化对于评估与肥胖相关的健康风险至关重要.
  • 目前用于脂肪组织细分的半自动化方法耗时,易受读器变异的影响.
  • 需要先进的计算技术来提高身体组成分析的效率和可靠性.

研究的目的:

  • 开发和评估一个软件工具,用于从腹部MRI数据自动化脂肪组织量化.
  • 将完全卷积网络 (FCN) 的性能与半自动参考方法进行比较.
  • 评估自动化方法的准确性,可靠性,处理量和时间效率.

主要方法:

  • 从肥胖患者的腹部MRI数据的回顾性分析.
  • 开发基于UNet的FCN架构,用于脂肪组织细分的数据增强.
  • 使用半自动感兴趣区域直方图值确定的基本事实.
  • 交叉验证使用相似性和错误指标来评估性能.

主要成果:

  • 对于皮下脂肪组织 (SAT) (高达0.954) 和内脏脂肪组织 (VAT) (高达0.889) FCN模型实现了高的子系数.
  • 卷度SAT和增值税评估显示出良好的相关性 (皮尔森的r>0.997),最小偏差 (<0.8%) 和低标准偏差 (<3.1%).
  • 高的类内相关系数 (SAT:0.999,增值税:0.996) 和低的变化系数 (SAT:1.4%,增值税:3.1%) 表示强大的可靠性.

结论:

  • 开发的基于FCN的软件提供准确可靠的自动化脂肪组织量化.
  • 与半自动化方法相比,这种自动化方法显著减少了处理力和读者依赖.
  • 深度学习技术,特别是FCN,非常适合在临床环境中进行常规的基于图像的身体成分分析.