TY - UNPB
T1 - WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans
AU - Hebert, Laetitia
AU - Ahamed, Tosif
AU - Costa, Antonio C.
AU - O'Shaugnessy, Liam
AU - Stephens, Greg J.
PY - 2020/8/15
Y1 - 2020/8/15
N2 - 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.
AB - 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.
UR - https://www.mendeley.com/catalogue/024e0a33-3dd5-31a3-bf94-3c7b0fc324a6/
U2 - 10.1101/2020.07.09.193755
DO - 10.1101/2020.07.09.193755
M3 - Preprint
VL - 2020
T3 - bioRxiv
SP - 1
EP - 16
BT - WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans
ER -