Tracking and analysis of caenorhabditis elegans behavior using machine vision

The behavior of the nematode C. elegans has proven increasingly useful for the genetic dissection of neurobiological signaling pathways and for investigating the neural and molecular basis of nervous system function. Locomotion is among the most complex aspects of C. elegans behavior, and involves a number of discrete motor activities such as omega bends (deep bends typically on the ventral side of the body which reorient the direction of forward locomotion), reversals (changes in the direction of the locomotion wave that cause a switch from forward to backward crawling), and foraging (a rapid, side-to-side movement of the nose). Here we use automated methods to automatically detect these activities, which rely in part on a new method for obtaining a morphological skeleton describing the body posture of coiled worms. These new methods have made it possible to reliably detect events that are time-consuming and laborious to detect by real-time observation or human video analysis. We also present an algorithm for tracking and distinguishing multiple C. elegans in a video sequence, including when they are in physical contact with one another. Our method makes it possible to identify two worms correctly before and after they touch each other, and to find the body poses for further feature extraction. The algorithm has many applications in the study of physical interactions between C. elegans.