Journal
CELL REPORTS
Volume 36, Issue 13, Pages -Publisher
CELL PRESS
DOI: 10.1016/j.celrep.2021.109730
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Funding
- National Science Foundation Graduate Research Fellowship
- University of Washington's Institute for Neuroengineering (UWIN)
- Center for Neurotechnology (CNT)
- National Institutes of Health [F31NS115477, R00 NS088193, DP2NS105555, R01NS111479, U19NS112959, R01NS102333, U19NS104655]
- Searle Scholars Program
- Pew Charitable Trusts
- McKnight Foundation
- Sloan Research Fellowship
- Washington Research Foundation
- Searle Scholar Program
- Pew Biomedical Scholar Program
- Tuthill labs
- Brunton lab
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Anipose is an open-source toolkit for robust markerless 3D pose estimation, built on the 2D tracking method DeepLabCut. It enables users to expand existing experimental setups for accurate 3D tracking. Analysis of 3D leg kinematics tracked with Anipose reveals the key role of joint rotation in motor control of fly walking.
Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals move in 3D. Here, we introduce Anipose, an opensource toolkit for robust markerless 3D pose estimation. Anipose is built on the 2D tracking method DeepLabCut, so users can expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on a calibration board as well as mice, flies, and humans. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking.
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