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Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
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Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
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使用四维动态MRI分析上呼吸道形态,使用基于激活深度学习的自动细分的四维动态MRI分析.

Cheng-Yang Yu1, Meng-Chen Chung1, Yunn-Jy Chen2,3

  • 1Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.

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概括
此摘要是机器生成的。

积极学习的nnU-Net在4DMRI上精确地对上呼吸道进行细分,揭示了口腔呼吸的形态学动态变化. 这种方法量化了性别和症状的变化,有助于了解呼吸道动态.

关键词:
积极学习是积极学习.深度学习是一种深度学习.自由呼吸的4D磁共振成像图像细分 图像细分打开嘴巴 呼吸 呼吸上空气道上空气道上空气道

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科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 呼吸系统解剖学 呼吸系统解剖学

背景情况:

  • 电影4DMRI捕捉了呼吸过程中的动态上呼吸道形态.
  • 积极学习的nnU-Net提高了细分的准确性,同时最大限度地减少了手动注释的努力.

研究的目的:

  • 开发一种自动化方法来对上呼吸道进行细分,使用在自由呼吸的cine 4D MRI上进行主动学习.
  • 在不同的口腔位置下量化上呼吸道形态的动态变化.

主要方法:

  • 一项涉及84名成年人 (33名有症状) 的前性横截面研究.
  • 使用了3T,自由呼吸的TWIST序列,具有闭口和开口的位置.
  • 采用积极学习的nnU-Net模型,通过放射科医生验证的手册注释进行培训.

主要成果:

  • 实现了高细分精度 (Dice 0.959 ± 0.019).
  • 开口呼吸显著改变了通气道的长度,并减少了后宫横截面积 (CSA).
  • 男性和有症状的个体表现出不同的气道体积和CSA特征,症状个体的动态变化更大.

结论:

  • 四维电影MRI与积极学习nnU-Net相结合,可以自动量化动态上呼吸道形态.
  • 该研究确定了口腔位置,症状和性别作为呼吸道形态和动态的独立预测因素.