A Generative Statistical Algorithm for Automatic Detection of Complex Postures

This paper presents a method for automated detection of complex (non-self-avoiding) postures of the nematode Caenorhabditis elegans and its application to analyses of locomotion defects. Our approach is based on progressively detailed statistical models that enable detection of the head and the body even in cases of severe coilers, where data from traditional trackers is limited. We restrict the input available to the algorithm to a single digitized frame, such that manual initialization is not required and the detection problem becomes embarrassingly parallel. Consequently, the proposed algorithm does not propagate detection errors and naturally integrates in a “big data” workflow used for large-scale analyses. Using this framework, we analyzed the dynamics of postures and locomotion of wild-type animals and mutants that exhibit severe coiling phenotypes. Our approach can readily be extended to additional automated tracking tasks such as tracking pairs of animals (e.g., for mating assays) or different species.Author Summary: The roundworm Caenorhabditis elegans is a widely used model organism. Its locomotion, for instance, enables the study of genetic and cellular mechanisms that underlie behavior and may be broadly conserved. Characterizing C. elegans locomotion requires identifying its body posture and tracking how posture changes with time. Existing machine vision approaches have greatly aided this effort. However, they have been limited in cases where the body of the animal curved strongly such that one part of the animal rested or slid against another part. We present a method for automated detection of such complex body postures and its application to the analysis of locomotion. At the core of our method are progressively detailed statistical models of the shape of the animal. These models enable us to assess the probability that a given image contains a suggested posture. Our approach does not require manual initialization and can be readily parallelized for large-scale applications. We used our approach to analyze locomotion in mutants that severely exaggerate their body bends, called coilers. This approach can readily be extended to additional automated tracking tasks such as pairs of interacting roundworms or different organisms.