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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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相关实验视频

Updated: Jul 1, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

数据驱动的显微镜的兴起,由机器学习提供动力.

Leonor Morgado^1,2, Estibaliz Gómez-de-Mariscal¹, Hannah S Heil¹

¹Optical Cell Biology, Instituto Gulbenkian de Ciência, Oeiras, Portugal.

Journal of microscopy

|March 6, 2024

概括

机器学习优化了生命科学领域的光学显微镜. 这种数据驱动的方法增强了实时图像分析,克服了传统技术的局限性,创造了新的实验可能性.

科学领域:

生命科学生命科学
生物技术是生物技术.
显微镜的使用方法

背景情况:

传统的光学显微镜在速度,分辨率,视野和光毒性方面面临着权衡.
数据驱动显微镜利用数据采集和分析之间的反循环来克服这些局限性.

研究的目的:

审查机器学习 (ML) 如何实现实时显微镜优化自动图像分析.
突出将ML集成到显微镜获取工作流程中的进步.

主要方法:

介绍数据驱动显微镜概念和相关的ML方法用于图像分析.
关于 ML 集成显微镜的开创性工作和最新进展的概述.

主要成果:

机器学习促进了显微镜参数的自动化,实时优化.
将ML集成到获取工作流中包括优化照明,获取速率和触发实验.

结论:

具有传感,分析和适应能力的智能显微镜有望彻底改变光学成像.
基于机器学习的方法为生命科学研究中的实验可能性开辟了新的途径.

关键词:

数据驱动的数据驱动.图像分析图像分析机器学习是机器学习.反应式显微镜反应式显微镜

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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