MSBOTS: a multiple small biological organism tracking system robust against non-ideal detection and segmentation conditions

Abstract

Accurately tracking a group of small biological organisms using algorithms to obtain their movement trajectories is essential to biomedical and pharmaceutical research. However, object mis-detection, segmentation errors and overlapped individual trajectories are particularly common issues that restrict the development of automatic multiple small organism tracking research. Extending on previous work, this paper presents an accurate and generalised Multiple Small Biological Organism Tracking System (MSBOTS), whose general feasibility is tested on three types of organisms. Evaluated on zebrafish, Artemia and Daphnia video datasets with a wide variety of imaging conditions, the proposed system exhibited decreased overall Multiple Object Tracking Precision (MOTP) errors of up to 77.59%. Moreover, MSBOTS obtained more reliable tracking trajectories with a decreased standard deviation of up to 47.68 pixels compared with the state-of-the-art idTracker system. This paper also presents a behaviour analysis module to study the locomotive characteristics of individual organisms from the obtained tracking trajectories. The developed MSBOTS with the locomotive analysis module and the tested video datasets are made freely available online for public research use.