DanioSense: Automated High-Throughput Quantification of Zebrafish Larvae Group Movement

The capability to obtain detailed motility information of model organisms is fundamental to reveal their functional and social behavior characteristics. Zebrafish is a powerful vertebrate model organism. Despite recent success in the automatic quantification of adult zebrafish movement, it remains a laborious task for group zebrafish larval tracking due to their similar appearance, frequent occlusions, and highly discontinuous kinematics. This article presents DanioSense (DS), an automatic tracker for group larval zebrafish, to overcome these tracking challenges. The integration of a light convolutional neural network and a centerline extraction algorithm enables the tracker to localize individuals even in occlusion cases where objects' identities are prone to switch. With reliable detections, an adaptive Kalman filter is designed to optimally estimate locomotive parameters, which is also used for object reidentification accomplished by a two-stage data association protocol. Experimental results demonstrated a tracking accuracy of over 97%, median errors of 102 mu m, and 8.8 degrees for the position and orientation measurement, and a processing speed of over 30 frames/s with a normal computer configuration. DS provides detailed quantitative data for a large-scale larvae group in nearly real time, highly boosting the efficiency of characterizing individual phenotypes and analyzing social interactions.

Automated summary

The study introduces DanioSense (DS), an automatic tracker for group larval zebrafish, that overcomes challenges such as similar appearances, frequent occlusions, and highly discontinuous kinematics through the integration of a light convolutional neural network and a centerline extraction algorithm. DS is capable of localizing individuals even in occlusion cases where object identities are prone to switch, providing detailed quantitative data for a large-scale larvae group in nearly real time.