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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...

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Updated: Jun 23, 2025

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

两个阶段的错误检测,以改善电子显微镜图像马赛克.

Jiahao Shi¹, Hongyu Ge², Shuohong Wang³

¹School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.

Computers in biology and medicine

|June 23, 2024

概括

准确地拼接大电子显微镜 (EM) 大脑连接组数据集是具有挑战性的. 本研究引入了一种两阶段错误检测方法,以提高EM图像马赛克的准确性和效率.

关键词:

电子显微镜的电子显微镜图像拼接图像拼接图像拼接关键点的特征是关键点的特征.拼接评估拼接评估

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Published on: August 22, 2019

Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy

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Published on: March 6, 2018

相关实验视频

Last Updated: Jun 23, 2025

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy

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Published on: March 6, 2018

科学领域:

神经科学是一个神经科学.
生物物理学的生物物理.
计算生物学计算生物学

背景情况:

大规模电子显微镜 (EM) 能够在突触水平上重建大脑连接体.
拼接庞大的EM图像数据集带来了重大的准确性挑战.
现有的方法,根据自然图像进行调整,对于EM数据来说是低效和容易出错的.

研究的目的:

开发一种准确有效的方法来拼接大规模的EM图像数据集.
在生物医学应用中解决传统图像拼接算法的局限性.
引入一种用于评估合EM图像质量的新型指标.

主要方法:

两个阶段的错误检测管道用于EM图像马赛克.
使用基于点的错误检测与混合功能框架的速度和准确性.
实施一个新的指标,EM拼接图像质量评估 (EMSIQA),用于拼接后错误的评估.

主要成果:

拟议的方法显著提高了EM图像马赛克的有效性.
管道实现了与现有方法相比较的高精度.
EMSIQA指标为合的EM图像提供了定量质量评估.

结论:

基于检测的新型马赛克管道提高了大规模EM数据处理的准确性和效率.
这种方法为重建详细的大脑连接组提供了更强大的解决方案.
EMSIQA的开发解决了评估生物医学EM图像拼接质量的关键需求.