PT  - JOURNAL ARTICLE
AU  - Laetitia Hebert
AU  - Tosif Ahamed
AU  - Antonio C. Costa
AU  - Liam O’Shaugnessy
AU  - Greg J. Stephens
TI  - WormPose: Image synthesis and convolutional networks for pose estimation in <em>C. elegans</em>
AID  - 10.1101/2020.07.09.193755
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.07.09.193755
4099  - http://biorxiv.org/content/early/2020/08/15/2020.07.09.193755.short
4100  - http://biorxiv.org/content/early/2020/08/15/2020.07.09.193755.full
AB  - 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 (∼ 10 hour), fast-sampled (∼ 30 Hz) recordings of on-food N2 worms to provide a posture-scale analysis of roaming/dwelling behaviors.Competing Interest StatementThe authors have declared no competing interest.