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Using DeepLabCut for 3D markerless pose estimation across species and behaviors
Tanmay Nath1, Alexander Mathis1,2, An Chi Chen3
1Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA.
Nature Protocols
|June 23, 2019
Summary
This study updates DeepLabCut, an open-source toolbox for markerless animal pose estimation. The enhanced version offers GUIs and active learning for precise behavioral tracking in scientific research.
Area of Science:
- Ethology
- Neuroscience
- Biomechanics
- Genetics
Background:
- Noninvasive behavioral tracking is crucial for scientific experiments.
- Markerless animal pose estimation is vital for analyzing behavior in various fields.
- Accurate pose extraction in dynamic environments remains a challenge.
Purpose of the Study:
- To introduce an updated version of the DeepLabCut toolbox.
- To enhance markerless animal pose estimation capabilities.
- To provide a user-friendly and efficient analysis pipeline.
Main Methods:
- Utilized a state-of-the-art human pose-estimation algorithm.
- Developed an open-source toolbox as a Python package.
- Incorporated graphical user interfaces (GUIs) and active-learning-based network refinement.
Main Results:
- The updated DeepLabCut enables precise tracking of user-defined features.
- New features include GUIs and performance improvements.
- A step-by-step procedure allows pipeline creation within 1-12 hours using a GPU.
Conclusions:
- The enhanced DeepLabCut toolbox offers improved markerless animal pose estimation.
- It facilitates the creation of tailored, reusable behavioral analysis pipelines.
- Accessible via Docker and cloud platforms like Google Colaboratory for broader scientific use.
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