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从未分割的医疗图像中弱监督的贝叶斯形状建模.

Jadie Adams1,2, Krithika Iyer1,2, Shireen Y Elhabian1,2

  • 1Scientific Computing and Imaging Institute, University of Utah, UT, USA.

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此摘要是机器生成的。

我们介绍了一种弱监督的深度学习方法,用于从医疗图像进行统计形状建模 (SSM). 这种方法使用点云数据,减少了大量手动注释的需求,并提高了创建SSM的可行性.

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科学领域:

  • 医学成像分析分析 医学成像分析
  • 计算解剖学的计算解剖学
  • 机器学习用于医疗保健

背景情况:

  • 统计形状建模 (SSM) 对临床研究至关重要,但传统方法复杂且容易产生偏见.
  • 深度学习已经从图像中改进了SSM预测,但依赖于完全监督的方法和繁的训练数据创建.

研究的目的:

  • 开发一种弱监督的深度学习方法,用医疗图像来预测SSM.
  • 减少在SSM建设中依赖手动注释和先前假设的依赖.

主要方法:

  • 调整了贝叶斯变量信息瓶深度SSM (BVIB-DeepSSM) 模型,以适应弱监管.
  • 使用点云表面表示用于监督,而不是地面真相对应点.
  • 以数据驱动的方式学习形状对应.

主要成果:

  • 实现了与完全监督方法可比的准确性和不确定性估计.
  • 显著提高了SSM模型培训的可行性,并降低了 SSM 模型培训的负担.
  • 在没有先前的可变性假设的情况下,展示了对函数学习的数据驱动方法.

结论:

  • 使用点云数据进行弱监督的深度学习为SSM构建提供了可行的替代方案.
  • 这种方法提高了临床研究中先进的形态测量分析的可访问性和实用性.
  • 这种方法有效地捕捉了解剖学变异,没有限制性的先前假设.