@article{5d7c412b87dc4c6d83303e624bbcb50b,
title = "Fast animal pose estimation using deep neural networks",
abstract = "The need for automated and efficient systems for tracking full animal pose has increased with the complexity of behavioral data and analyses. Here we introduce LEAP (LEAP estimates animal pose), a deep-learning-based method for predicting the positions of animal body parts. This framework consists of a graphical interface for labeling of body parts and training the network. LEAP offers fast prediction on new data, and training with as few as 100 frames results in 95% of peak performance. We validated LEAP using videos of freely behaving fruit flies and tracked 32 distinct points to describe the pose of the head, body, wings and legs, with an error rate of <3% of body length. We recapitulated reported findings on insect gait dynamics and demonstrated LEAP{\textquoteright}s applicability for unsupervised behavioral classification. Finally, we extended the method to more challenging imaging situations and videos of freely moving mice.",
author = "Pereira, {Talmo D.} and Aldarondo, {Diego E.} and Lindsay Willmore and Mikhail Kislin and Wang, {Samuel S.H.} and Mala Murthy and Shaevitz, {Joshua W.}",
note = "Funding Information: The authors acknowledge J. Pillow for discussions; B.C. Cho for contributions to the acquisition and preprocessing pipeline for mouse experiments; P. Chen for a previous version of a neural network for pose estimation that was useful in designing our method; H. Jang, M. Murugan, and I. Witten for feedback on the GUI and other discussions; G. Guan for assistance maintaining flies; and the Murthy, Shaevitz and Wang labs for general feedback. This work was supported by the NIH R01 NS104899-01 BRAIN Initiative Award and an NSF BRAIN Initiative EAGER Award (to M.M. and J.W.S.), NIH R01 MH115750 BRAIN Initiative Award (to S.S.-H.W. and J.W.S.), the Nancy Lurie Marks Family Foundation and NIH R01 NS045193 (to S.S.-H.W.), an HHMI Faculty Scholar Award (to M.M.), NSF GRFP DGE-1148900 (to T.D.P.), and the Center for the Physics of Biological Function sponsored by the National Science Foundation (NSF PHY-1734030). Publisher Copyright: {\textcopyright} 2018, The Author(s), under exclusive licence to Springer Nature America, Inc.",
year = "2019",
month = jan,
day = "1",
doi = "10.1038/s41592-018-0234-5",
language = "English (US)",
volume = "16",
pages = "117--125",
journal = "Nature Methods",
issn = "1548-7091",
publisher = "Nature Publishing Group",
number = "1",
}