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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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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相关实验视频

Updated: Jun 22, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

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多源数据集成用于分割未注释的MRI图像.

Navapat Nananukul, Hamid Soltanian-Zadeh, Mohammad Rostami

    IEEE journal of biomedical and health informatics
    |July 2, 2024
    PubMed
    概括

    这项研究引入了无监督的联合域适应用于磁共振成像 (MRI) 细分,减少了对专家放射科医生的注释的需求. 该方法将知识从多个标记的MRI数据集转移到未标记的域,从而提高了细分的准确性.

    科学领域:

    • 医疗成像医学成像
    • 人工智能的人工智能
    • 计算机视觉 计算机视觉

    背景情况:

    • 深度神经网络增强磁共振成像 (MRI) 细分用于临床应用.
    • 训练这些模型需要大量的注释数据,这是耗时和昂贵的.
    • 患者,扫描仪和协议之间的MRI数据的变化需要特定领域的再培训和专家注释.

    研究的目的:

    • 开发一种无监督的联合域调整方法,以克服在MRI细分中需要持久的数据注释的需求.
    • 为了使知识从多个注释源域转移到一个没有注释的目标域.
    • 为了提高MRI细分的深度学习模型的概括性和减少注释负担.

    主要方法:

    • 使用多个注释源域进行无监督的联合域调整.
    • 在潜伏嵌入空间中最小化目标和源域之间的对智能分布距离.
    • 采用整体方法来整合来自所有领域的知识,以改善细分.

    主要成果:

    • 在两个实验数据集上证明了拟议方法的有效性.
    • 成功地将知识从注释领域转移到未注释领域.
    • 减少对新领域的专家放射科医生的手动注释的依赖.

    更多相关视频

    Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
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    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
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    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

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    相关实验视频

    Last Updated: Jun 22, 2025

    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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    Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
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    Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
    04:25

    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

    Published on: December 15, 2023

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    结论:

    • 开发的无监督联合域适应方法有效地解决了MRI细分中的数据注释挑战.
    • 该方法促进了跨多个MRI数据领域的知识转移,提高了模型的适用性.
    • 公共可用的代码可以在自动化医疗图像细分方面进行进一步的研究和开发.