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 Statement The authors have declared no competing interest. Footnotes * Added quantitative comparisons with Broekmans et al (eLife 5, e17227, 2016) and corrected an error in Fig. 5(B). * https://github.com/iteal/wormpose...

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

10.1101/2020.07.09.193755