The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Fast animal pose estimation using deep neural networks
Open accessPublished version of a preprint
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’s applicability for unsupervised behavioral classification. Finally, we extended the method to more challenging imaging situations and videos of freely moving mice. LEAP is a deep-learning-based approach for the analysis of animal pose. LEAP’s graphical user interface facilitates training of the deep network. The authors illustrate the method by analyzing Drosophila and mouse behavior.
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Metadata
- Authors
- Pereira, T. D., Aldarondo, D. E., Willmore, L., Kislin, M., Wang, S. S.-H., Murthy, M., & Shaevitz, J. W.
- Venue
- Nature Methods
- Published
- 2018-12-20
- Volume
- 16
- Issue
- 1
- Pages
- 117-125
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2952956973
- Cited by
- 687
- References
- 54
- FWCI
- 28.79
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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