Using DeepLabCut for 3D markerless pose estimation across species and behaviors
Using DeepLabCut for 3D markerless pose estimation across species and behaviors
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DOI：
10.1038/s41596-019-0176-0
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发表时间：
2019-07-01
期刊：
影响因子：
14.8
通讯作者：
Mathis, Mackenzie Weygandt
中科院分区：
文献类型：
--
作者：
Nath, Tanmay;Mathis, Alexander;Mathis, Mackenzie Weygandt
在实验过程中对动物进行无创行为跟踪是许多科学研究的关键。在生物力学、遗传学、行为学和神经科学中，不使用标记提取动物的姿势通常是测量行为效应的必要条件。然而，在动态变化的背景中提取没有标记的详细姿势一直是一个挑战。我们最近推出了一个名为DeepLabCut的开源工具箱，它建立在最先进的人体姿势估计算法的基础上，允许用户用有限的训练数据训练深度神经网络，以精确跟踪与人类标签准确性相匹配的用户定义特征。在这里，我们提供了一个更新的工具箱，作为Python包开发，其中包括图形用户界面（gui）、性能改进和基于主动学习的网络优化等新特性。我们提供了使用DeepLabCut的分步程序，指导用户在1-12小时（取决于帧大小）内使用图形处理单元（GPU）创建量身定制的可重用分析管道。此外，我们还提供了Docker环境和Jupyter notebook，可以在谷歌collaboration等云资源上运行。
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 activelearning-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.