The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609 USA.
Commun Biol. 2019 Mar 29;2:124. doi: 10.1038/s42003-019-0362-1. eCollection 2019.
The ability to track animals accurately is critical for behavioral experiments. For video-based assays, this is often accomplished by manipulating environmental conditions to increase contrast between the animal and the background in order to achieve proper foreground/background detection (segmentation). Modifying environmental conditions for experimental scalability opposes ethological relevance. The biobehavioral research community needs methods to monitor behaviors over long periods of time, under dynamic environmental conditions, and in animals that are genetically and behaviorally heterogeneous. To address this need, we applied a state-of-the-art neural network-based tracker for single mice. We compare three different neural network architectures across visually diverse mice and different environmental conditions. We find that an encoder-decoder segmentation neural network achieves high accuracy and speed with minimal training data. Furthermore, we provide a labeling interface, labeled training data, tuned hyperparameters, and a pretrained network for the behavior and neuroscience communities.
准确追踪动物对于行为实验至关重要。对于基于视频的分析，这通常通过操纵环境条件来增加动物与背景之间的对比度来实现，以实现适当的前景/背景检测（分割）。为了实现实验的可扩展性而改变环境条件，会违背行为学的相关性。生物行为研究界需要能够在长时间内、在动态环境条件下、在遗传和行为上具有异质性的动物中监测行为的方法。为了满足这一需求，我们为单只老鼠应用了一种最先进的基于神经网络的追踪器。我们比较了三种不同的神经网络架构在视觉差异较大的老鼠和不同环境条件下的表现。我们发现，编码器-解码器分割神经网络在使用最小训练数据的情况下可以实现高精度和快速跟踪。此外，我们为行为和神经科学界提供了一个标记接口、标记训练数据、调优的超参数和一个预训练的网络。
