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

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Greg J Stephens, Tosif Ahamed, Laetitia Hebert, Antonio C Costa, Liam O'Shaughnessy
Journal/Conference Name bioRxiv 2020 7
Paper Category
Paper 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 (~10 hour), fast-sampled (~30 Hz) recordings of on-food N2 worms to provide a posture-scale analysis of roaming/dwelling behaviors.
Date of publication 2020
Code Programming Language Python

Copyright Researcher 2022