WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans

Laetitia Hebert, Tosif Ahamed, Antonio C. Costa, Liam O’Shaughnessy, Greg J. Stephens*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


An important model system for understanding genes, neurons and behavior, the nematode worm C. elegans naturally moves through a variety of complex postures, for which estimation from video data is challenging. We introduce an open-source Python package, WormPose, for 2D pose estimation in C. elegans, including self-occluded, coiled shapes. We leverage advances in machine vision afforded from convolutional neural networks and introduce a synthetic yet realistic generative model for images of worm posture, thus avoiding the need for human-labeled training. WormPose is effective and adaptable for imaging conditions across worm tracking efforts. We quantify pose estimation using synthetic data as well as N2 and mutant worms in on-food conditions. We further demonstrate WormPose by analyzing long (* 8 hour), fast-sampled (* 30 Hz) recordings of on-food N2 worms to provide a posture-scale analysis of roaming/dwelling behaviors.

Original languageEnglish
Article numbere1008914
Pages (from-to)1-20
Number of pages20
JournalPLoS Computational Biology
Issue number4
Early online date27 Apr 2021
Publication statusPublished - Apr 2021

Bibliographical note

Publisher Copyright:
© 2021 Hebert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Copyright 2021 Elsevier B.V., All rights reserved.


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