Behavioral assays such as the Y-maze, depend on manual arena segmentation, strict lighting conditions, and labor-intensive video scoring. These limitations impede reproducibility, delay data acquisition, and restrict access for laboratories without extensive computational infrastructure.
Researchers at the University of Florida have developed software to address challenges in the analysis of the Y-maze behavioral testing platform. This Y-maze tracking software enables automated, high-throughput behavioral analysis using real-time zone segmentation and robust image processing.. Unlike currently available software, the automated nature of this software, coupled with improved accuracy and consistency in behavioral quantification while significantly reduces manual labor and technical barriers associated with similar workflows.
Software that enables high-throughput Y-maze tracking through real-time zone segmentation, providing robust and consistent behavioral analysis across large datasets
The Y-maze behavioral tracking module processes video frames using OpenCV and HSV-based color segmentation to isolate the maze structure. It detects mouse position through frame-difference and pixel-thresholding within predefined zones, logs zone entries, path metrics, and dwell times for hundreds of files in under two hours, and outputs positional and cumulative heatmaps. The software is optimized for standard computing environments and designed to streamline behavioral data acquisition with minimal user intervention.