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进步的深度SSM:图像到形状深度模型的培训方法

Abu Zahid Bin Aziz1,2, Jadie Adams1,2, Shireen Elhabian1,2

  • 1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.

Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.)
|May 15, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了渐进式DeepSSM,这是一种用于深度学习模型的新型培训策略,用于从医疗图像中创建统计形状模型 (SSM). 这种方法提高了解剖形状分析的准确性和稳定性.

关键词:
深度监督 深度监督医疗成像医学成像渐进式学习 渐进式学习统计形状建模 统计形状建模

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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
  • 医疗保健中的深度学习

背景情况:

  • 统计形状建模 (SSM) 对于医学上解剖形状的定量分析至关重要.
  • 直接使用3D医疗图像进行SSM建设需要大量的预处理.
  • 目前用于从图像直接构建SSM的深度学习方法表现不佳.

研究的目的:

  • 提出一种新的培训策略,即渐进的DeepSSM,以改进图像对形状分析的深度学习模型.
  • 为了能够逐步学习粗细两种解剖形状特征.
  • 提高直接从未分割的医学图像生成的SSM的准确性和稳定性.

主要方法:

  • 引入了一个名为渐进式DeepSSM的多层次培训策略.
  • 模型培训通过多个尺度进行,每个尺度都建立在前一个尺度上.
  • 用分段引导的多任务学习和深度监督损失来结合形状先验并确保每个规模的学习.

主要成果:

  • 用渐进式DeepSSM策略训练的模型表现出优越的定量和质量性能.
  • 拟议的策略显著提高了统计形状表示的准确性.
  • 使用这种方法的深度学习模型观察到增强的培训稳定性.

结论:

  • 渐进式DeepSSM是一种有效的训练方法,用于基于深度学习的图像到形状模型.
  • 这一策略可以广泛采用,以改进用于解剖形状分析的现有深度学习方法.
  • 这种方法可以更准确,更稳定地直接从医学图像中推断统计形状模型.