An Automated Micropositioning System for Investigating C. elegans Locomotive Behavior

This paper presents a visually servoed micropositioning system capable of automatically extracting locomotive features of Caenorhabditis elegans online at a full 30Hz. The employment of Gaussian Pyramid Level-2 images significantly reduces the image size by 16-fold and permits real-time feature extraction, without sacrificing accuracy due to the cubic smoothing spline fitting. The automated micropositioning system is capable of revealing subtle differences in locomotive behavior across strains. A total of 128 worms of four C. elegans strains with different numbers of muscle arms were continuously tracked for 3min per sample, and locomotive features were extracted online. Validated by experiments, the innovation in image analysis, or data reduction without sacrificing accuracy allows for rapid, online, and accurate analysis of streaming videos of C. elegans and other similar microorganisms.