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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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Related Experiment Video

Updated: Aug 26, 2025

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Published on: June 28, 2017

A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data.

Guolan Lu^1,2,3, Marc A Baertsch^2,3,4, John W Hickey^2,3

¹Department of Otolaryngology, Stanford University School of Medicine, Stanford, CA, United States.

Frontiers in Immunology

|October 10, 2022

Summary

This summary is machine-generated.

RAPID software accelerates the processing of large-scale, high-dimensional multiplexed fluorescence microscopy data. This GPU-accelerated tool enhances image quality and reduces processing time for robust spatial biology analysis.

Keywords:

CODEX imaging GPU acceleration big data drift compensation highly multiplexed imaging image deconvolution image processing parallel computing

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Last Updated: Aug 26, 2025

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

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Published on: June 28, 2017

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Published on: June 20, 2020

Area of Science:

Spatial Biology
Microscopy
Bioinformatics

Background:

Multiplexed, single-cell imaging generates terabytes of data, crucial for understanding cellular interactions in health and disease.
Efficient processing of high-dimensional imaging data is vital for accurate cell segmentation, identification, and neighborhood analysis.

Purpose of the Study:

To introduce RAPID, a Real-time, GPU-Accelerated Parallelized Image processing software.
To address the challenges of processing large-scale, high-dimensional multiplexed fluorescence microscopy data.

Main Methods:

RAPID utilizes GPU acceleration for parallelized image processing.
Key functions include deconvolution, image stitching, registration with drift correction, and autofluorescence minimization.

Main Results:

RAPID achieves results comparable to commercial software using open-source CUDA-driven deconvolution.
The software significantly reduces data processing time and artifacts.
RAPID improves image contrast and signal-to-noise ratio.

Conclusions:

RAPID provides a robust and efficient tool for analyzing large-scale, multiplexed fluorescence imaging data.
The software facilitates accurate segmentation, cell identification, and mechanistic insights in spatial biology.