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

You might also read

Related Articles

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

Sort by

Same author

CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images.

Nature protocols·2026

Same author

A lightweight network for segmenting tree-like structures in medical images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same author

Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration.

GigaScience·2026

Same author

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

Nature communications·2026

Same author

Artificial intelligence (AI)-powered imaging for objective psoriasis severity assessment at home: Feasibility and reliability in a randomized clinical trial.

Journal of the American Academy of Dermatology·2026

Same author

Multimodal animal health monitoring in extensive livestock production systems.

Frontiers in veterinary science·2026

Same journal

Probabilistic RNA designability via interpretable ensemble approximation and dynamic decomposition.

Bioinformatics (Oxford, England)·2026

Same journal

Quantifying domain-specific relevance of computational biology Wikipedia articles using TF-IDF and cosine similarity.

Bioinformatics (Oxford, England)·2026

Same journal

GATSBI: improving context-aware protein embeddings through biologically motivated data splits.

Bioinformatics (Oxford, England)·2026

Same journal

BiMba: using Vision Mamba to predict protein sites that bind other proteins.

Bioinformatics (Oxford, England)·2026

Same journal

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

Bioinformatics (Oxford, England)·2026

Same journal

Is a Win-Win possible? Achieving pareto-optimal privacy-utility balance in fine-tuned genome language model embeddings against embedding reconstruction attacks.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 3, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

A benchmark for comparison of cell tracking algorithms.

Martin Maška¹, Vladimír Ulman², David Svoboda²

¹Center for Biomedical Image Analysis, Masaryk University, 602 00 Brno, Czech Republic, Cancer Imaging Laboratory, Oncology Division, Center for Applied Medical Research, University of Navarra, 31008 Pamplona, Spain, Biomedical Imaging Group Rotterdam, Erasmus University Medical Center, 3015 GE Rotterdam, The Netherlands, Fusion Technology and Systems Department, Compunetix Inc., Monroeville, PA 15146, USA, Biomedical Computer Vision Group, Department of Bioinformatics and Functional Genomics, University of Heidelberg, BIOQUANT, IPMB and DKFZ, 69120 Heidelberg, Germany, KTH Royal Institute of Technology, ACCESS Linnaeus Center, Department of Signal Processing, 100 44 Stockholm, Sweden, Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Image Processing, Leiden University Medical Center, 2300 RC Leiden, The Netherlands, Institute of Cellular Biology and Pathology, First Faculty of Medicine, Charles University in Prague, 12801 Prague 2, Czech Republic and Biomedical Image Technologies, Universidad Politécnica de Madrid & CIBER BBN, 28040 Madrid, SpainCenter for Biomedical Image Analysis, Masaryk University, 602 00 Brno, Czech Republic, Cancer Imaging Laboratory, Oncology Division, Center for Applied Medical Research, University of Navarra, 31008 Pamplona, Spain, Biomedical Imaging Group Rotterdam, Erasmus University Medical Center, 3015 GE Rotterdam, The Netherlands, Fusion Technology and Systems Department, Compunetix Inc., Monroeville, PA 15146, USA, Biomedical Computer Vision Group, Department of Bioinformatics and Functional Genomics, University of Heidelberg, BIOQUANT, IPMB and DKFZ, 69120 Heidelberg, Germany, KTH Royal Institute of Technology, ACCESS Linnaeus Center, Department of Signal Processing, 100 44 Stockholm, Sweden, Baxter Laboratory for Stem Cell Biology, Department of Microbiology and

Bioinformatics (Oxford, England)

|February 15, 2014

Summary

This summary is machine-generated.

A new benchmark for evaluating cell tracking algorithms in microscopy was created. This framework objectively compares and ranks various methods using diverse datasets, aiding future research in biomedical imaging.

More Related Videos

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

Author Spotlight: Understanding Disease Mechanisms Through Real-Time Analysis of T-Cell Migration

Author Spotlight: Understanding Disease Mechanisms Through Real-Time Analysis of T-Cell Migration

Published on: May 24, 2024

Related Experiment Videos

Last Updated: May 3, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

Author Spotlight: Understanding Disease Mechanisms Through Real-Time Analysis of T-Cell Migration

Author Spotlight: Understanding Disease Mechanisms Through Real-Time Analysis of T-Cell Migration

Published on: May 24, 2024

Area of Science:

Biomedical Imaging
Cell Biology
Computational Biology

Background:

Automatic cell tracking in multidimensional time-lapse fluorescence microscopy is crucial for biomedical research.
A novel framework for objective evaluation of cell tracking algorithms was established via the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge.
This work presents the logistics, datasets, methods, and results of the challenge, establishing principles for future benchmark use.

Purpose of the Study:

To introduce a standardized benchmark for evaluating cell tracking algorithms.
To compare and rank different cell tracking algorithms based on objective performance measures.
To provide a comprehensive resource for the cell tracking research community.

Main Methods:

Development of a comprehensive video dataset repository for training and competition.
Definition of objective measures for algorithm comparison and ranking.
Evaluation of six algorithms with diverse segmentation and tracking paradigms on synthetic and real datasets.

Main Results:

A benchmark was created, including a dataset repository and objective evaluation metrics.
Six cell tracking algorithms were compared and ranked based on their performance.
Results were analyzed separately for each dataset due to their diversity, without declaring a single overall winner.

Conclusions:

The Cell Tracking Challenge established a valuable benchmark for objective algorithm evaluation.
The benchmark facilitates comparison of diverse cell tracking methods on standardized datasets.
This resource supports the advancement of automated cell tracking in microscopy-based research.