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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

4.8K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
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Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

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Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
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相关实验视频

Updated: Jul 16, 2025

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
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使用深度学习对生物样品进行单次多光谱定量相成像.

Sunil Bhatt, Ankit Butola, Anand Kumar

    Applied optics
    |September 14, 2023
    PubMed
    概括

    这项研究引入了一个深度神经网络,从单个干扰图中生成多谱定量相成像 (MS-QPI). 这种先进的技术使生物和光学样本能够快速,无标签地进行形态分析.

    科学领域:

    • 生物医学光学 生物医学光学
    • 定量的相位成像成像技术
    • 显微镜中的深度学习.

    背景情况:

    • 多光谱定量相成像 (MS-QPI) 提供高对比度,无标签的形态分析.
    • 提取光谱依赖的定量信息通常需要多次测量.

    研究的目的:

    • 使用MS-QPI开发一种一次性方法,以提取使用MS-QPI的光谱依赖量化信息.
    • 利用深层神经网络从单个干涉图生成多光谱相位图.

    主要方法:

    • 使用数字全息显微镜,使用三个波长 (532,633和808纳米).
    • 在光学波导和MG63细胞的干涉度数据上训练了一个生成对抗网络 (GAN).
    • 从单个输入干涉图生成多谱 (MS) 定量相图.

    主要成果:

    • 通过使用训练有素的GAN.成功生成了准确的MS相位图.
    • 通过将预测阶段地图与数值重建地图 (FT+TIE) 进行比较来验证方法.
    • 使用已建立的评估指标量化图像质量.

    结论:

    • 开发的深度学习方法可以实现高效的,一次性MS-QPI.

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  • 这种方法显著提升了无标签的形态成像能力.
  • 该技术显示了分析各种生物和光学样本的潜力.