The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Using DeepLabCut for 3D markerless pose estimation across species and behaviors
Open accessAbstract
Noninvasive behavioral tracking of animals during experiments is critical to many scientific pursuits. Extracting the poses of animals without using markers is often essential to measuring behavioral effects in biomechanics, genetics, ethology, and neuroscience. However, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open-source toolbox called DeepLabCut that builds on a state-of-the-art human pose-estimation algorithm to allow a user to train a deep neural network with limited training data to precisely track user-defined features that match human labeling accuracy. Here, we provide an updated toolbox, developed as a Python package, that includes new features such as graphical user interfaces (GUIs), performance improvements, and active-learning-based network refinement. We provide a step-by-step procedure for using DeepLabCut that guides the user in creating a tailored, reusable analysis pipeline with a graphical processing unit (GPU) in 1–12 h (depending on frame size). Additionally, we provide Docker environments and Jupyter Notebooks that can be run on cloud resources such as Google Colaboratory. This protocol describes how to use an open-source toolbox, DeepLabCut, to train a deep neural network to precisely track user-defined features with limited training data. This allows noninvasive behavioral tracking of movement.
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Cites
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Cited by
DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning
Graving, J. M., Chae, D., Naik, H., Li, L., Koger, B., Costelloe, B. R., & Couzin, I. D. (2019) · eLife · doi:10.7554/elife.47994 · Cited by 547
Deep learning tools for the measurement of animal behavior in neuroscience
Mathis, M. W., & Mathis, A. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.10.008 · Cited by 450
Self-citationComputational Neuroethology: A Call to Action
Datta, S. R., Anderson, D. J., Branson, K., Perona, P., & Leifer, A. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.038 · Cited by 453
Anipose: A toolkit for robust markerless 3D pose estimation
Karashchuk, P., Rupp, K. L., Dickinson, E. S., Walling-Bell, S., Sanders, E., Azim, E., Brunton, B. W., & Tuthill, J. C. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109730 · Cited by 324
B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors
Hsu, A. I., & Yttri, E. A. (2021) · Nature Communications · doi:10.1038/s41467-021-25420-x · Cited by 298
Metadata
- Authors
- Nath, T., Mathis, A., Chen, A. C., Patel, A., Bethge, M., & Mathis, M. W.
- Venue
- Nature Protocols
- Published
- 2019-06-21
- Volume
- 14
- Issue
- 7
- Pages
- 2152-2176
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W2950100524
- Cited by
- 1,505
- References
- 64
- FWCI
- 52.62
- Type
- journal article
- Language
- en
- Open access
- green
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- Nath, T., Mathis, A., Chen, A. C., Patel, A., Bethge, M., & Mathis, M. W. (2019). Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nature Protocols. https://doi.org/10.1038/s41596-019-0176-0
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