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

• Published in: PLoS computational biology Vol. 17; no. 4; p. e1008914
• Main Authors: Hebert, Laetitia, Ahamed, Tosif, Costa, Antonio C., O’Shaughnessy, Liam, Stephens, Greg J.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Behavior; Biology and Life Sciences; Caenorhabditis elegans; Computer and Information Sciences; Engineering and Technology; Gene expression; Genetic aspects; Inspection; Labeling; Methods; Morphology; Motor task performance; Nematodes; Neural networks; Noise; Occlusion; Optimization; Physical Sciences; Physiological aspects; Pose estimation; Posture; Principal components analysis; Research and Analysis Methods; Resolution; Search algorithms; Social Sciences; Canada; Netherlands
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1008914

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.