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相关概念视频

Fixation and Sectioning01:03

Fixation and Sectioning

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Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...
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相关实验视频

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Histological-Based Stainings Using Free-Floating Tissue Sections
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Histological-Based Stainings Using Free-Floating Tissue Sections

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使用机器学习进行无化学物质的组织组织染色.

Julie A Renner1, Patrick C Riley1

  • 1US Army DEVCOM Chemical Biological Center, Aberdeen Proving Ground, MD, USA.

Journal of histotechnology
|April 22, 2024
PubMed
概括

人工智能,特别是CycleGAN,可以在没有配对数据的情况下几乎染色未染色的组织图像. 该方法为数字病理学中危险和昂贵的化学染色提供了一个有希望的替代方案.

科学领域:

  • 数字病理学数字病理学
  • 计算机成像成像技术
  • 医学中的人工智能

背景情况:

  • 传统的血素和素 (H&E) 染色是危险的,昂贵的和变化的.
  • 人工智能 (AI) 和机器学习 (ML),包括生成对抗网络 (GAN),提供虚拟染色解决方案.
  • 现有的GAN如DCGAN和CGAN通常需要注册,配对的图像,这些图像很难获得.

研究的目的:

  • 应用无监督的CycleGAN pix2pix模型进行虚拟的H&E染色.
  • 从未配对的,未染色的明亮场图像中生成病理学家批准的数字染色图像.
  • 为了克服现有的虚拟染色方法中对照图像要求的局限性.

主要方法:

  • 使用了一个无监督的CycleGAN pix2pix模型与u-net架构.
  • 在未配对,未染色的明亮场和化学染色的甲固定嵌入肝脏样本图像上训练模型.
  • 实现了循环一致的损失来处理未配对的图像数据集.

主要成果:

  • 从未染色的明亮场图像中成功生成数字"染色"图像.
  • 证明了使用CycleGAN与未配对数据用于虚拟H&E染色的可行性.
  • 这代表了这种架构对未配对的明亮场图像的首次记录应用.
关键词:
人工智能/机器学习循环GANAN是一个循环.美国H&E公司没有化学物质的组织学.数字数字数字数字数字数字数字.像素2pixxxx 在线观看没有配对的图像.虚拟染色是一种虚拟染色.

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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections
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A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections

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相关实验视频

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A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections
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A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections

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结论:

  • 无监督的CycleGAN为传统的H&E染色提供了一个可行的替代方案.
  • 这种方法消除了对危险化学品和配对图像数据集的需求.
  • 讨论了进一步的研究和建议的改进,以优化虚拟染色技术.