WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans |
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Authors: | Laetitia Hebert Tosif Ahamed Antonio C Costa Liam OShaughnessy Greg J Stephens |
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Institution: | 1. Biological Physics Theory Unit, OIST Graduate University, Onna, Japan ; 2. Lunenfeld-Tanenbaum Research Institute, University of Toronto, Toronto, Canada ; 3. Department of Physics & Astronomy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands ; Hebrew University of Jerusalem, ISRAEL |
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Abstract: | 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. |
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