Open AccessWormPose: Image synthesis and convolutional networks for pose estimation in C. elegans
Author(s) -
Laetitia Hebert,
Tosif Ahamed,
Á. S. Costa,
Liam O’Shaughnessy,
Greg J. Stephens
Publication year - 2021
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1008914
Subject(s) - pose , artificial intelligence , convolutional neural network , computer science , leverage (statistics) , python (programming language) , computer vision , robustness (evolution) , deep learning , pattern recognition (psychology) , 3d pose estimation , machine learning , biology , biochemistry , gene , operating system
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.