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相关概念视频

Super-resolution Fluorescence Microscopy01:37

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Overview of Microscopy Techniques01:22

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The early pioneers of microscopy opened a window into the invisible world of microorganisms. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes that leveraged nonvisible light, such as fluorescence microscopy that uses an ultraviolet light source and electron microscopy that uses short-wavelength electron beams. These advances significantly improved magnification, image resolution, and contrast. By comparison, the...
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Confocal Fluorescence Microscopy01:16

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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
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相关实验视频

Updated: Sep 11, 2025

Highly Resolved Intravital Striped-illumination Microscopy of Germinal Centers
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Highly Resolved Intravital Striped-illumination Microscopy of Germinal Centers

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学习,不确定性驱动的适应性获取用于光子效率扫描显微镜.

Cassandra Tong Ye, Jiashu Han, Kunzan Liu

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    概括
    此摘要是机器生成的。

    这项研究引入了一种深度学习方法,用于在量化预测不确定性时消除扫描显微镜图像的模糊性. 这种方法使不确定区域的适应性扫描成为可能,大大减少了成像时间和光照.

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    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
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    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
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    科学领域:

    • 生物医学成像技术 生物医学成像技术
    • 计算生物学 计算生物学
    • 机器学习 机器学习

    背景情况:

    • 扫描显微镜 (共聚焦,多光子) 可以进行深层组织成像,但在速度,视野,光毒性和图像质量之间面临权衡.
    • 深度学习提供了否定的,但也有可能产生幻觉的文物,这对科学和医学应用来说是有问题的.

    研究的目的:

    • 开发一种可靠的深度学习方法,用于扫描显微镜,同时消除图像和量化像素的不确定性.
    • 引入适应性采集策略,利用不确定性地图来优化成像时间和减少光剂量.

    主要方法:

    • 开发了一种新的深度学习方法,用于对显微镜数据进行同时的无声化和像素智能不确定性预测.
    • 提出了一种适应性获取技术,利用学习的不确定性地图来指导对不确定样本区域的重新扫描.
    • 该方法在实验共聚焦和多光子显微镜数据上得到了验证.

    主要成果:

    • 不确定性地图准确地识别了基于深度学习的图像中的幻觉.
    • 通过适应性采集技术,采集时间和总光剂量减少了16倍.
    • 精细的样本特征被成功恢复,幻觉被减少了适应性方法.

    结论:

    • 这项工作首次展示了无分布的不确定性量化方法,用于与实实验显微镜数据进行测量.
    • 基于重建不确定性的拟议适应性获取策略在成像效率和样品安全方面提供了显著的改进.