Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

5.2K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
5.2K

You might also read

Related Articles

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

Sort by
Same author

FtsW protein-protein interactions visualized in live Staphylococcus aureus cells by FLIM-FRET.

Nature communications·2026
Same author

Mechanical deformation inhibits growth and migration of <i>S. aureus</i> within submicrometer channels.

mBio·2026
Same author

Quantitative Mapping of Nanoscale EGFR-Grb2 Assemblies by DNA-PAINT.

Chemphyschem : a European journal of chemical physics and physical chemistry·2026
Same author

Customizable FDM-based zebrafish larva mold for live imaging.

Biology open·2026
Same author

Wash-Free Multi-Target Super-Resolution Microscopy With Photocaged DNA Labels.

Angewandte Chemie (International ed. in English)·2026
Same author

Quantitative Stimulated Emission Depletion (STED) Microscopy with DNA-Fluorophore Labels.

ACS nano·2026
Same journal

Ciliary flow and morphology shape mass transport at the surface and within gastrovascular cavities of black corals.

Communications biology·2026
Same journal

Virus-mediated prokaryotic community adaptation dynamics under thermal stress in municipal organic solid waste microbiomes.

Communications biology·2026
Same journal

Multi-omics insights into the woolly trait of Saussurea medusa and the plant's coordinated regulation of flavonoid biosynthesis.

Communications biology·2026
Same journal

Loss contexts enhance dorsolateral prefrontal interpersonal neural synchrony during successful human deceptive recommendations.

Communications biology·2026
Same journal

Neuro-regulator role of H<sub>2</sub>S in astrocyte activation and its effects on neurological damage and behavior of VPA-exposed rats.

Communications biology·2026
Same journal

Temporal orchestration of transcriptional and epigenomic programming underlying maternal embryonic diapause in a cricket model.

Communications biology·2026
See all related articles

Related Experiment Video

Updated: Sep 5, 2025

Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues
08:01

Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues

Published on: March 1, 2024

1.1K

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.

Christoph Spahn1,2, Estibaliz Gómez-de-Mariscal3, Romain F Laine4,5,6

  • 1Department of Natural Products in Organismic Interaction, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany. christoph.spahn@mpi-marburg.mpg.de.

Communications Biology
|July 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces the ZeroCostDL4Mic platform, enabling researchers to analyze bacterial microscopy images using artificial neural networks. It provides a comprehensive guide and dataset for deep learning applications in microbiology, facilitating image analysis and biological insights.

More Related Videos

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
06:03

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

534
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Related Experiment Videos

Last Updated: Sep 5, 2025

Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues
08:01

Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues

Published on: March 1, 2024

1.1K
AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
06:03

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

534
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Area of Science:

  • Microbiology
  • Computational Biology
  • Biotechnology

Background:

  • Bacterial imaging is crucial for understanding cell biology and antibiotic effects.
  • Analyzing complex microscopy data often requires specialized expertise and computational resources.
  • Deep learning (DL) offers powerful tools for image analysis but requires accessible platforms and curated datasets.

Purpose of the Study:

  • To demonstrate and guide the application of artificial neural networks for bacterial microscopy image analysis.
  • To introduce the ZeroCostDL4Mic platform and its associated database for DL model training.
  • To showcase DL applications in segmentation, classification, phenotypic profiling, and image enhancement for microbiology research.

Main Methods:

  • Utilized state-of-the-art artificial neural networks within the ZeroCostDL4Mic platform.
  • Generated a database of image datasets for training DL models for various analysis tasks.
  • Applied DL for bright field/fluorescence image segmentation, object detection in time-lapse data, and phenotypic profiling.
  • Demonstrated DL-based image denoising for low-phototoxicity live-cell microscopy.
  • Showcased artificial labeling and super-resolution image prediction for cell structure analysis.

Main Results:

  • Successfully applied DL for segmenting diverse bacterial images (bright field and fluorescence).
  • Enabled classification of bacterial growth stages using object detection in time-lapse imaging.
  • Facilitated DL-assisted phenotypic profiling of antibiotic-treated bacterial cells.
  • Improved live-cell microscopy by enhancing image quality through denoising, allowing faster and longer imaging.
  • Achieved accurate mapping of cell shape and intracellular targets via artificial labeling and super-resolution prediction.

Conclusions:

  • The ZeroCostDL4Mic platform and curated database empower novice users to apply DL for bacterial image analysis.
  • This work promotes efficient DL application in microbiology, fostering tool development for bacterial cell biology and antibiotic research.
  • The study highlights the potential of DL to accelerate discoveries in microbial imaging and related fields.