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深度SSM:图像到形状深度学习模型的蓝图.

Riddhish Bhalodia1, Shireen Elhabian1, Jadie Adams1

  • 1Scientific Computing and Imaging Institute, 72 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA; School of Computing, 50 Central Campus Dr, University of Utah, Salt Lake City, UT 84112, USA.

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概括

深度学习框架DeepSSM通过直接映射3D图像到形状描述器来自动化统计形状建模 (SSM). 与传统方法相比,这大大减少了计算时间和手工劳动.

关键词:
对应模型的对应模型.深度学习是一种深度学习.统计形状建模 统计形状建模

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科学领域:

  • 医疗图像分析 医学图像分析
  • 计算解剖学的计算解剖学
  • 机器学习在医疗保健中的应用

背景情况:

  • 统计形状建模 (SSM) 对于分析医学图像中的解剖变异至关重要.
  • 传统的SSM需要广泛的预处理,包括细分和注册,需要大量的人力和计算资源.
  • 现有的方法需要重复复杂的管道来获取新数据,从而限制了效率.

研究的目的:

  • 介绍DeepSSM,这是一个深度学习框架,用于端到端的图像到形状建模.
  • 为了自动地从3D医疗图像中直接提取低维形状描述符和表示.
  • 克服传统SSM中手动预处理和计算负担的局限性.

主要方法:

  • 开发了DeepSSM,这是一个深度学习框架,可以从图像中学习映射,以塑造描述符和表示.
  • 实施基于模型的数据增强策略,以解决数据稀缺问题.
  • 在三个医疗数据集上评估了两个DeepSSM架构变体,具有不同的损失函数.

主要成果:

  • DeepSSM成功地从3D图像中直接推断出解剖学的统计表示.
  • 该框架显著减少了计算时间,并消除了手动细分的需要.
  • 在定量和应用驱动的评估中,DeepSSM在定量和应用驱动的评估中实现了与最先进的SSM方法相提并论或更高的性能.

结论:

  • DeepSSM为统计形状建模应用提供了可行,高效和自动化的解决方案.
  • 该框架为基于深度学习的图像到形状模型提供了一个全面的蓝图.
  • 通过简化形状分析,DeepSSM展示了更广泛的临床应用的潜力.