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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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基于MRI的质母细胞瘤细分的模式冗余.

Selene De Sutter1, Joris Wuts2,3, Wietse Geens4

  • 1Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium. selene.de.sutter@vub.be.

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概括

使用较少的MRI方式 (如T1CE-FLAIR) 进行质母细胞瘤细分,可以达到与使用所有四种方法相美的准确性. 减少MRI细分的输入方式是可行的,并可能改善临床适用性.

关键词:
布拉特斯 (BraTS) 是一个很好的表现.深度学习是一种深度学习.质母细胞瘤 (glioblastoma) 是一个磁共振成像 (MRI) 是一种磁共振成像技术.分段化 分段化 分段化 分段化不确定性 不确定性

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科学领域:

  • 放射学 放射学是一门学科.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 自动化质母细胞瘤细分通常使用四种MRI模式:T1,对比增强T1 (T1CE),T2和FLAIR.
  • 这些模式中的冗余性可能会降低模型性能,并增加与临床环境中缺少数据相关的风险.

研究的目的:

  • 调查不同MRI模式对质母细胞瘤细分精度的相关性和影响.
  • 为了确定是否减少的输入集可以实现与标准的四种模式输入相比较的细分性能.

主要方法:

  • 训练了使用nnU-Net和SwinUNETR架构的多重细分模型,并使用不同组合的MRI输入模式.
  • 基于细分精度和认识体系不确定性的评估模型性能.

主要成果:

  • 使用T1CE或T1CE-FLAIR进行细分,可以达到与特定瘤区域的全部四种模式输入相美的准确度.
  • nnU-Net显示T1CE-FLAIR-T1的峰值精度,这表明更多输入的潜在冗余问题.
  • SwinUNETR在三输入模型和全输入模型之间显示了统计学上同等的结果.

结论:

  • 使用T1CE-FLAIR的最小输入模型是质母细胞瘤细分的一个可行的替代方案.
  • 除了T1CE-FLAIR之外的添加方式并不能始终提高准确性,并且可能会降低准确性,尽管它可以减少细分不确定性.