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Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

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...

Protein Dynamics in Living Cells

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

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Updated: Sep 16, 2025

Fluorescence Lifetime Imaging of Molecular Rotors in Living Cells

Fluorescence Lifetime Imaging of Molecular Rotors in Living Cells

Published on: February 9, 2012

通过一维深度学习优化快速光终身成像.

Xinwei Gao¹, Yanfeng Liu¹, Yong Guo¹

¹State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University); College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen University, Shenzhen 518060, P. R. China.

Analytical chemistry

|July 11, 2025

概括

此摘要是机器生成的。

使用1D频道注意力卷积神经网络 (1D CANNs) 的新深度学习方法显著加快了光终身成像 (FLIM) 分析. 这种高效的方法减少了计算负载,并提高了生物医学应用的准确性.

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Visualizing Protein Kinase A Activity In Head-fixed Behaving Mice Using In Vivo Two-photon Fluorescence Lifetime Imaging Microscopy

Visualizing Protein Kinase A Activity In Head-fixed Behaving Mice Using In Vivo Two-photon Fluorescence Lifetime Imaging Microscopy

Published on: June 7, 2019

Fluorescence Lifetime Macro Imager for Biomedical Applications

Fluorescence Lifetime Macro Imager for Biomedical Applications

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相关实验视频

Last Updated: Sep 16, 2025

Fluorescence Lifetime Imaging of Molecular Rotors in Living Cells

Fluorescence Lifetime Imaging of Molecular Rotors in Living Cells

Published on: February 9, 2012

Visualizing Protein Kinase A Activity In Head-fixed Behaving Mice Using In Vivo Two-photon Fluorescence Lifetime Imaging Microscopy

Visualizing Protein Kinase A Activity In Head-fixed Behaving Mice Using In Vivo Two-photon Fluorescence Lifetime Imaging Microscopy

Published on: June 7, 2019

Fluorescence Lifetime Macro Imager for Biomedical Applications

Fluorescence Lifetime Macro Imager for Biomedical Applications

Published on: April 7, 2023

科学领域:

生物医学光学生物医学光学
计算生物学是一种计算生物学.
深度学习应用程序深度学习应用程序

背景情况:

传统的光终身成像 (FLIM) 分析是计算密集的.
现有的方法往往需要复杂的数据拟合技术.
需要更快,更有效的FLIM数据处理.

研究的目的:

开发一种硬件效率高的深度学习方法,用于快速分析FLIM数据.
为了提高FLIM的速度和减少计算要求.
在FLIM和相关的成像技术上验证1D CANNs的性能.

主要方法:

利用了一维通道注意力卷积神经网络 (1D CANNs).
在原始与时间相关的单光子计数 (TCSPC) 数据上训练有素的1D CANN.
应用 1D CANNs 进行光寿命配合,光寿命成像 (PLIM) 和 STED-FLIM.

主要成果:

在使用1D CANNs的FLIM数据处理中实现了高效率和速度.
在预测和地面真相生命周期地图之间表现出强大的一致性.
在PLIM中获得了10%的预测错误,并在STED-FLIM中改善了空间分辨率.

结论:

1D CANNs为FLIM分析提供了一个计算效率高,准确的替代方案.
该方法在各种生物医学和光子学应用中显示出广泛的潜力,包括疾病诊断和先进的成像.
1D CANNs在不同的光子计数条件下具有强大性能.