Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Reduced conversion and readmission rates in robotic ileocecal resection for Crohn's disease: a propensity-matched analysis of the Hugo™ RAS system.

Surgical endoscopy·2026

Same author

Reliable classification of polyps based on artificial intelligence: a development and validation study.

EClinicalMedicine·2026

Same author

Validation of Digital Cytology for Primary Diagnosis Across a Range of Specimen Types.

Cytopathology : official journal of the British Society for Clinical Cytology·2026

Same author

Age disparity in robotic colorectal surgery: overcoming surgeon hesitancy to improve elderly patient outcomes.

Journal of robotic surgery·2026

Same author

Applicability of mitotic figure counting by deep learning: a development and pan-cancer validation study.

FEBS open bio·2026

Same author

Negative Selection Maintains Grossly Altered but Broadly Stable Karyotypes in Metastatic Colorectal Cancer.

Cancer discovery·2026

Same journal

Mapping Microvascular Flow via Radon Transform Ultrasound: Technical Advances and Pilot Application.

BME frontiers·2026

Same journal

Automated HER2 Scoring with Uncertainty Quantification Using Lensfree Holography and Deep Learning.

BME frontiers·2026

Same journal

Prospect of Wearable Ultrasound.

BME frontiers·2026

Same journal

Erratum to "Synergistic Antibacterial Activity of Fe<sub>3</sub>O<sub>4</sub>@mPEG-Ag Nanoparticles with Molecular Docking Analyses".

BME frontiers·2026

Same journal

A Hemoglobin-Based Nanoparticle Delivery System Enhances the Pharmacokinetics and Efficacy of Tigecycline in <i>Klebsiella pneumoniae</i> Infections.

BME frontiers·2026

Same journal

Machine Learning-Powered fNIRS Detection of Idiopathic Central Precocious Puberty via Prefrontal Cortex Activation.

BME frontiers·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 9, 2026

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

VS-FPM: Large-Format, Label-Free Virtual Histopathology Microscopy.

Christopher Bendkowski¹, Adam P Levine^2,3, Manuel Rodriguez-Justo^2,3

¹UCL Hawkes Institute and Department of Computer Science, University College London, London, UK.

|December 4, 2025

Summary

This summary is machine-generated.

Virtual staining using Fourier ptychographic microscopy (VS-FPM) creates realistic H&E images from unstained tissues. This label-free digital pathology method enables accurate diagnoses and overcomes limitations of traditional histopathology.

More Related Videos

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

Related Experiment Videos

Last Updated: Jan 9, 2026

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

Area of Science:

Digital Pathology
Microscopy
Machine Learning

Background:

Virtual staining (VS) generates realistic histological images from label-free data.
VS can streamline workflows, improve consistency, and enable novel tissue analysis.
Fourier ptychographic microscopy (FPM) offers high resolution and large fields of view.

Purpose of the Study:

To develop and assess a novel VS method (VS-FPM) using supervised machine learning.
To generate brightfield H&E images from FPM phase images of unstained tissues.
To evaluate VS-FPM for colonic polyp diagnosis.

Main Methods:

Trained a conditional generative adversarial network to translate FPM images to H&E images.
Utilized unstained tissue samples for image acquisition.
Assessed diagnostic accuracy using colonic polyp cases.

Main Results:

VS-FPM achieved spatial resolution comparable to traditional scanners.
VS-FPM images closely resembled chemically stained H&E images.
Pathologists accurately diagnosed normal vs. dysplastic tissues using VS-FPM.

Conclusions:

VS-FPM is a reliable and accessible virtual staining method.
The technique overcomes limitations of conventional histopathology microscopy.
VS-FPM offers advantages for label-free digital pathology.