Robust mouse tracking in complex environments using neural networks

Brian Geuther, Sean P. Deats, Kai J. Fox, Steve A. Murray, Robert E. Braun, Jacqueline K. White, Elissa J. Chesler, Cathleen M. Lutz, Vivek Kumar

Abstract:

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.