An unsupervised learning approach for tracking mice in an enclosed area

BACKGROUND: In neuroscience research, mouse models are valuable tools to understand the genetic mechanisms that advance evidence-based discovery. In this context, large-scale studies emphasize the need for automated high-throughput systems providing a reproducible behavioral assessment of mutant mice with only a minimum level of manual intervention. Basic element of such systems is a robust tracking algorithm. However, common tracking algorithms are either limited by too specific model assumptions or have to be trained in an elaborate preprocessing step, which drastically limits their applicability for behavioral analysis. RESULTS: We present an unsupervised learning procedure that is basically built as a two-stage process to track mice in an enclosed area using shape matching and deformable segmentation models. The system is validated by comparing the tracking results with previously manually labeled landmarks in three setups with different environment, contrast and lighting conditions. Furthermore, we demonstrate that the system is able to automatically detect non-social and social behavior of interacting mice. The system demonstrates a high level of tracking accuracy and clearly outperforms the MiceProfiler, a recently proposed tracking software, which serves as benchmark for our experiments. CONCLUSIONS: The proposed method shows promising potential to automate behavioral screening of mice and other animals. Therefore, it could substantially increase the experimental throughput in behavioral assessment automation.