LaZeTrack: An open-source object detection algorithm to detect and track 2-day-old zebrafish motor touch responses

Zebrafish are a model organism used for the study of vertebrate development, disease, and drug discovery. Two-day old zebrafish exhibit burst swimming behavior that can be elicited by a light touch to the tail. Motor touch responses are frequently video-recorded and later analyzed by hand. Despite this being a simple behavioral assay, methods for robustly analyzing these videos in a reproducible and time-efficient manner rely on manual tracking, which is prone to experimenter bias and error. Here we present LaZeTrack, a machine learning-based program, which employs Ultralytics' YOLOv8 nano (YOLOv8n) object detection algorithm to automatically analyze touch response videos using 2-day-old zebrafish. This program breaks down video files into their constituent frames which are then passed through a custom-trained YOLOv8n algorithm to detect the presence of a single zebrafish (mean average precision, mAP50-95 = 0.74). LaZeTrack then refines the tracking data output by the model and tabulates it into an Excel spreadsheet. It then computes and extracts four relevant swim metrics: swim duration (s), swim distance (mm), mean swim velocity (mm/s), and maximum swim velocity (mm/s). LaZeTrack rapidly accelerates the analysis process, while also eliminating errors associated with manual tracking. Furthermore, it allows for high-throughput analysis of zebrafish touch response videos and can detect subtle differences in motor metrics arising from temperature differences, demonstrating the utility of this tracking algorithm. LaZeTrack is available for download on GitHub (Armstrong-Lab-70/LaZeTrack).