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相关概念视频

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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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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Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

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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: Jul 16, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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两阶段深度学习方法用于稀疏视图光分子断层扫描重建.

Xuanxuan Zhang, Yunfei Jia, Jiapei Cui

    Journal of the Optical Society of America. A, Optics, image science, and vision
    |September 14, 2023
    PubMed
    概括

    这项研究引入了一种深度学习方法,用于更快的光分子断层扫描 (FMT) 成像,使用更少的视图. 这种新的方法有效地从稀疏的投影数据中重建图像,提高速度而不会牺牲质量.

    科学领域:

    • 临床前的光学成像技术
    • 分子成像学分子成像学
    • 生物医学工程 生物医学工程

    背景情况:

    • 光分子断层扫描 (FMT) 能够在细胞/分子层面追踪生理和病理过程.
    • 减少FMT投影视图可以提高数据采集速度,这对于动态研究至关重要.
    • 越来越少的视图加剧了FMT反向问题的错误性质,降低了图像质量.

    研究的目的:

    • 开发一种基于深度学习的重建方法,用于稀疏视野的FMT.
    • 为了实现高速的FMT成像,只使用四个垂直投影视图.
    • 为了解决由FMT中减少投影视图所造成的图像退化问题.

    主要方法:

    • 为稀疏视图FMT重建提出了两阶段的深度学习方法.
    • 第一个阶段:一个完全卷积的神经网络恢复了表面光投影的视图,减轻了光子扩散模糊.
    • 第二阶段:一个卷积神经网络执行反向拉顿转换,从恢复的投影中重建横切片.

    主要成果:

    • 拟议的深度学习方法有效地从稀疏的FMT投影视图中重建图像.
    • 数字模拟,幻影实验和小鼠研究证实了该方法的有效性.
    • 该技术成功地解决了稀疏视图FMT中的图像重建挑战.

    更多相关视频

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    Last Updated: Jul 16, 2025

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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    Deep Learning-Based Segmentation of Cryo-Electron Tomograms

    Published on: November 11, 2022

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    Lensless Fluorescent Microscopy on a Chip
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    Lensless Fluorescent Microscopy on a Chip

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    Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
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    Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

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    结论:

    • 开发的深度学习方法为稀疏视图FMT重建提供了有效的解决方案.
    • 这种方法通过减少投影视图来显著提高成像速度.
    • 该方法对推进动态临床前分子成像应用有前途.