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

Laetitia Hebert, Tosif Ahamed, Antonio C. Costa, Liam O'Shaugnessy, Greg J. Stephens

Research output: Contribution to JournalArticleAcademic


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.
Original languageEnglish
Pages (from-to)1-16
Number of pages16
Early online date10 Jul 2020
Publication statusPublished - 15 Aug 2020


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