anTraX, a software package for high-throughput video tracking of color-tagged insects.

ants collective behavior ethology formicidae machine learning machine vision neuroscience social behavior social insects tracking

Journal

eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614

Informations de publication

Date de publication:
19 11 2020
Historique:
received: 22 04 2020
accepted: 29 10 2020
entrez: 19 11 2020
pubmed: 20 11 2020
medline: 19 3 2021
Statut: epublish

Résumé

Recent years have seen a surge in methods to track and analyze animal behavior. Nevertheless, tracking individuals in closely interacting, group-living organisms remains a challenge. Here, we present anTraX, an algorithm and software package for high-throughput video tracking of color-tagged insects. anTraX combines neural network classification of animals with a novel approach for representing tracking data as a graph, enabling individual tracking even in cases where it is difficult to segment animals from one another, or where tags are obscured. The use of color tags, a well-established and robust method for marking individual insects in groups, relaxes requirements for image size and quality, and makes the software broadly applicable. anTraX is readily integrated into existing tools and methods for automated image analysis of behavior to further augment its output. anTraX can handle large-scale experiments with minimal human involvement, allowing researchers to simultaneously monitor many social groups over long time periods.

Identifiants

pubmed: 33211008
doi: 10.7554/eLife.58145
pii: 58145
pmc: PMC7676868
doi:
pii:

Types de publication

Evaluation Study Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NIGMS NIH HHS
ID : R35 GM127007
Pays : United States
Organisme : Howard Hughes Medical Institute
Pays : United States
Organisme : Human Frontier Science Program
ID : LT001049/2015
Pays : International

Informations de copyright

© 2020, Gal et al.

Déclaration de conflit d'intérêts

AG, JS, DK No competing interests declared

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Auteurs

Asaf Gal (A)

Laboratory of Social Evolution and Behavior, The Rockefeller University, New York, United States.

Jonathan Saragosti (J)

Laboratory of Social Evolution and Behavior, The Rockefeller University, New York, United States.

Daniel Jc Kronauer (DJ)

Laboratory of Social Evolution and Behavior, The Rockefeller University, New York, United States.

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Classifications MeSH