Search research articles

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...

Three-Dimensional Microscopy in Microbiology

Three-Dimensional Microscopy in Microbiology

Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...

Two-Dimensional Microscopy in Microbiology

Two-Dimensional Microscopy in Microbiology

Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...

You might also read

Related Articles

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

Sort by

Same author

Search, organize, aggregate and share image data with BioFile Finder (BFF).

Nature methods·2026

Same author

Implicit neural image field for biological microscopy image compression.

Nature computational science·2025

Same author

Is the Apple Vision Pro the Ultimate Display? A First Perspective and Survey on Entering the Wonderland of Precision Medicine.

JMIR serious games·2024

Same author

Stroke and myocardial infarction induce neutrophil extracellular trap release disrupting lymphoid organ structure and immunoglobulin secretion.

Nature cardiovascular research·2024

Same author

Multimodal large language models for bioimage analysis.

Nature methods·2024

Same author

Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories.

bioRxiv : the preprint server for biology·2024

Same journal

The quest for early detection of retinal disease: 3D CycleGAN-based translation of optical coherence tomography into confocal microscopy.

Biological imaging·2025

Same journal

Topology-based segmentation of 3D confocal images of emerging hematopoietic stem cells in the zebrafish embryo.

Biological imaging·2025

Same journal

Exploring self-supervised learning biases for microscopy image representation.

Biological imaging·2025

Same journal

Deep-blur: Blind identification and deblurring with convolutional neural networks.

Biological imaging·2025

Same journal

Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ.

Biological imaging·2025

Same journal

Seeing or believing in hyperplexed spatial proteomics via antibodies: New and old biases for an image-based technology.

Biological imaging·2024

See all related 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

Search research articles

Related Experiment Video

Updated: Jun 3, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Deep-learning-based image compression for microscopy images: An empirical study.

Yu Zhou^1,2, Jan Sollmann^1,2, Jianxu Chen¹

¹Department of Biospectroscopy, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.

Biological Imaging

|January 8, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI)-based image compression significantly outperforms traditional methods for large bioimaging datasets. These advanced techniques minimize impact on downstream deep learning tasks, like label-free prediction, ensuring data integrity.

Keywords:

compression deep learning in-silico labelling microscopic images

More Related Videos

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

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

Related Experiment Videos

Last Updated: Jun 3, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

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

Area of Science:

Computational Biology
Bioimaging Data Science
Artificial Intelligence in Microscopy

Background:

Rapid advancements in microscopy generate massive bioimaging datasets, straining data infrastructure.
Image compression is crucial for managing large volumes of imaging data.
The impact of compression on downstream deep learning models remains an open question.

Purpose of the Study:

To analyze and compare classic and deep learning-based image compression methods.
To empirically study the impact of these compression techniques on downstream deep learning models.
To evaluate compression effects on label-free prediction tasks using microscopy images.

Main Methods:

Comparison of multiple classic and AI-based image compression algorithms.
Empirical evaluation using deep learning-based label-free prediction models (bright-field to fluorescent image prediction).
Analysis metrics included compression ratio, image similarity, and downstream model prediction accuracy.

Main Results:

AI-based compression techniques demonstrated superior performance over classic methods.
Deep learning compression showed minimal negative influence on the accuracy of 2D label-free prediction tasks.
Compression ratio and image similarity varied significantly across different methods.

Conclusions:

Deep learning-based image compression offers a promising solution for managing large bioimaging datasets.
These advanced methods preserve data utility for critical downstream deep learning applications.
Awareness of compression's impact on deep learning models is essential for bioimaging data analysis.