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 Experiment Video

Updated: May 19, 2026

Quantification of Acanthamoeba spp. Motility
07:33

Quantification of Acanthamoeba spp. Motility

Published on: September 20, 2024

Automated Optimization of Bacterial Tracking Pipelines With TrackMate 8.

Marie Anselmet1,2, Laura Xénard1,3, Marvin Albert1,4

  • 1Institut Pasteur, Image Analysis Hub, Université Paris Cité, Paris, France.

Current Protocols
|May 18, 2026
PubMed

Related Concept Videos

Automated Microbial Diagnostics01:24

Automated Microbial Diagnostics

Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...

You might also read

Related Articles

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

Sort by
Same author

Arf1 is involved in Neisseria meningitidis-induced cortical branched F-actin network reorganization.

EMBO reports·2026
Same author

Functional dissection of <i>Corynebacterium glutamicum</i> Wag31 domains for septal recruitment and polar distribution during the cell cycle.

mBio·2026
Same author

SAMJ: fast image annotation on ImageJ/Fiji via segment anything model.

Nature communications·2026
Same author

A connection between two ancient and essential cellular processes, iron-sulfur protein biogenesis and fatty acid synthesis, in <i>Escherichia coli</i>.

mBio·2026
Same author

The microbiota affects energy production, nitrogen excretion, and sterol metabolism in mosquito larvae.

mBio·2026
Same author

The orphan receptor GPR84 drives inflammation in Buruli ulcer development.

Science translational medicine·2026
Same journal

Streamlined In Vitro Transcription for Generating Self-Amplifying RNA With Modified Nucleotides.

Current protocols·2026
Same journal

CODEC Library Preparation From Genomic DNA.

Current protocols·2026
Same journal

Standardized Pipeline for Metabolism and Cognition in GO-DS21 Mouse Model: Investigating Down Syndrome Comorbidities.

Current protocols·2026
Same journal

Effect of Passive Hamstring Stretching on Craniovertebral Angle, Muscle Activation Pattern and Proprioception in Individuals with Forward Head Posture: Study Protocol for Randomized Controlled Trial.

Current protocols·2026
Same journal

Standardization of Immunohistochemistry Using an Anti-WASLB Antibody in the Skeletal Muscle of Teleost Fish.

Current protocols·2026
Same journal

Zebrafish Embryos to Profile Nano(bio)materials: A Modular Platform for Developmental Toxicity, Neurotoxicity, and Inflammation-Regeneration Assays.

Current protocols·2026
See all related articles
Summary
This summary is machine-generated.

This study introduces an automated method using TrackMate 8 to optimize bacterial tracking in microscopy. It helps microbiologists select the best algorithms and parameters for accurate quantitative analysis of bacterial dynamics.

Area of Science:

  • Microbiology
  • Bioimaging
  • Computational Biology

Background:

  • Quantitative analysis of bacterial dynamics using time-lapse microscopy relies on effective tracking algorithms.
  • Selecting and optimizing these algorithms for specific experiments presents a significant challenge for microbiologists.

Purpose of the Study:

  • To present an automated methodology for determining optimal tracking configurations in microbiological applications.
  • To enhance the TrackMate Fiji plugin with microbiology-specific tools for improved bacterial image analysis.

Main Methods:

  • The methodology is based on TrackMate 8, integrating deep-learning algorithms (Omnipose, YOLO, Trackastra) suitable for bacterial images.
  • It includes a TrackMate-Helper extension for parameter optimization and a tracking/segmentation editor for ground-truth generation.
Keywords:
automated parameter tuningbacterial trackingbioimage analysiscell trackinglive imagingmicrobiology image analysismicroscopy

More Related Videos

Real-Time In Vitro Migration Assay for Primary Murine CD8+ T Cells
06:42

Real-Time In Vitro Migration Assay for Primary Murine CD8+ T Cells

Published on: May 24, 2024

Related Experiment Videos

Last Updated: May 19, 2026

Quantification of Acanthamoeba spp. Motility
07:33

Quantification of Acanthamoeba spp. Motility

Published on: September 20, 2024

Real-Time In Vitro Migration Assay for Primary Murine CD8+ T Cells
06:42

Real-Time In Vitro Migration Assay for Primary Murine CD8+ T Cells

Published on: May 24, 2024

  • The approach systematically evaluates algorithm-parameter combinations based on biologically relevant metrics like cell-cycle accuracy and bacterial morphology.

    • Main Results:

    • Demonstrated the effectiveness and adaptability of the methodology across diverse experimental conditions using two use cases.
    • Successfully integrated advanced deep-learning models into the tracking pipeline for enhanced segmentation and tracking accuracy.
    • Enabled systematic optimization of tracking parameters, leading to improved biologically relevant metrics.

    Conclusions:

    • The developed methodology provides microbiologists with a widely applicable and automated framework for optimizing bacterial tracking pipelines.
    • Facilitates more accurate and efficient quantitative analysis of bacterial dynamics in microscopy images.
    • Advances the field of microbial imaging by streamlining complex data analysis processes.