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Super-resolution Fluorescence Microscopy

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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 developed.

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Related Experiment Video

Updated: Jun 23, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Bridging annotated microscopy imaging data and analysis method development for scientific discovery.

Kevin A Yamauchi^1,2, Virginie Uhlmann^3,4

¹Department of Biosystems Science and Engineering, ETH Zurich, Basel, CH, Switzerland.

Patterns (New York, N.Y.)

|June 22, 2026

Summary

This summary is machine-generated.

Annotated image datasets are crucial for advancing computational analysis in microscopy. Sharing these datasets, along with open standards and infrastructure, accelerates scientific discovery and innovation in biological imaging.

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Area of Science:

Microscopy imaging
Computational biology
Data science

Background:

Modern imaging technologies generate complex, high-dimensional biological data.
Computational analysis methods struggle to keep pace with data generation.
Developing analysis tools requires interdisciplinary expertise.

Purpose of the Study:

To highlight the importance of annotated image datasets in microscopy.
To discuss how data sharing drives progress in computational analysis.
To emphasize the need for open data standards and infrastructure.

Main Methods:

Review of progress in computational analysis inspired by adjacent fields.
Discussion on the role of annotated datasets as ground truth.
Emphasis on open data standards and infrastructure.

Main Results:

Annotated image datasets are essential for developing and validating microscopy image analysis methods.
Sharing annotated datasets has demonstrably improved computational analysis.
Open data standards and infrastructure are critical for maximizing dataset utility.

Conclusions:

Fostering a collaborative ecosystem of data, infrastructure, and analysis methods is key.
This ecosystem will elevate research quality and accelerate innovation in biological imaging.
Community-wide participation is essential for cultivating this dynamic environment.