@article{oai:oist.repo.nii.ac.jp:00002141,
 author = {Hebert, Laetitia and Ahamed, Tosif and Costa, Antonio C. and O’Shaughnessy, Liam and Stephens, Greg J.},
 issue = {4},
 journal = {PLOS Computational Biology},
 month = {Apr},
 note = {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 ( approximately ～8 hour), fast-sampled ( approximately ～ 30 Hz) recordings of on-food N2 worms to provide a posture-scale analysis of roaming/dwelling behaviors.},
 title = {WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans},
 volume = {17},
 year = {2021}
}