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相关概念视频

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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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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 6, 2025

Fluorescence Lifetime Imaging of Molecular Rotors in Living Cells
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使用卷积神经网络提高光寿命成像显微镜相位器的精度.

Varun Mannam1, Jacob P Brandt2, Cody J Smith2

  • 1Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, United States.

Frontiers in bioinformatics
|January 8, 2024
PubMed
概括
此摘要是机器生成的。

本研究使用预训练的卷积神经网络 (CNN) 来有效地消除光终身成像显微镜 (FLIM) 数据,改善生物成像应用的信号噪声比 (SNR) 和细分精度.

关键词:
卷积神经网络 (CNN) 是一种神经网络.深度学习是一种深度学习.光终身成像显微镜 (FLIM) 的应用图像分割 图像细分 图像细分生命周期图像分析阶段或分类集群方法.阶段子生命周期合成

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科学领域:

  • * 生物医学成像技术
  • * 光显微镜的使用
  • * 图像成像中的机器学习

背景情况:

  • *光终身成像显微镜 (FLIM) 是一种强大的生物成像技术.
  • * FLIM面临的挑战包括信号噪声比 (SNR) 低,采集速度缓慢和复杂性.
  • *改善SNR对于准确分析和解释FLIM数据至关重要.

研究的目的:

  • * 为了解决FLIM图像中低SNR的挑战.
  • * 展示使用预训练的卷积神经网络 (CNN) 进行 FLIM 数据的否定.
  • *为了提高光体分离和图像细分的准确性.

主要方法:

  • *使用预先训练有素的CNN模型来拒绝FLIM测量,消除了对广泛训练数据集的需求.
  • * 在推断阶段使用预训练网络,以实现快速计算 (毫秒) 和高精度.
  • * 应用K-means集群到细分,无色化图像以进行光体分离.

主要成果:

  • * 在FLIM图像中通过各种生物样本 (体内小鼠脏,固定细胞,固定小鼠脏) 证明有效的降噪.
  • *表现出更好的细分精度和增强的SNR,即使在具有挑战性的,分布之外的条件下 (体内植物样本).
  • *验证了该方法在噪音高的FLIM图像中分离光体的有效性,特别是在体内应用中.

结论:

  • * 拟议的方法提供了一个快速而准确的方法来对任何系统的FLIM图像进行细分.
  • * 显著改善生物医学成像中生物相关结构的识别.
  • * 提供了强大的解决方案,以拒绝FLIM数据,特别是在无法实现平均化的场景中.