Automated Tracking of Multiple C. Elegans

Abstract

Abstract-This paper presents a method for model based automated tracking of multiple worm-like creatures. These methods are essential for accurate quantitative analysis into the genetic basis of behavior that involve more than one organism. An accurate worm model is designed using the geometry of planar curves and nonlinear estimation of the model's parameters are performed using a central difference Kalman filter (CDKF). The filter can naturally be expanded to estimate the locations of multiple worms and determine when they are occluding each other. The predicted location of the models at each iteration allows for an efficient method to determine the regions that are undergoing occlusions. Experiments on actual C. Elegans mating sequence data demonstrate the quality of the proposed method.

Strategies for automated analysis of C. elegans locomotion

“…These solutions perform considerably better than the purely bottom-up approaches used in machine vision of C. elegans (Fontaine et al, 2006). Given that there are constraints on the shapes that worms can adopt, as well as on the rate at which those shapes can change, a system in which model worms are matched to an image can be envisaged, and would be expected to perform better in situations where most existing processes fail, notably worm coiling, aggregates of worms and omega turns.…”

“…One computational model (Roussel et al, 2007) combines hypothesis tracking (following several possible hypotheses that interpret the image sequence and selecting the most likely) with an energy barrier approach which encapsulates the observation that when worms collide they usually prefer to pass side-by-side rather than to cross over. In another study, the Central Diffuse Kalman Filter, which has generally proved powerful in many computer vision applications to identify partly occluded shapes, was combined with worm models to allow interpretation of images of worms partly occluding each other (Fontaine et al, 2006). The application of these algorithms results in more robust measurements, and by allowing interacting worms to be tracked, it may be possible to capture the effects of mutations on social behaviours, as well as increase the range of parameter space over which new phenotypes can be identified.…”

Image Tracking of Multiple C. Elegans Worms Using Adaptive Scanning Optical Microscope (ASOM)

“…Other techniques such as those described in Fontaine et al (2006), Huang et al (2007), and Roussel et al (2007), rely on a parametric model of the worm to predict its motion. In Fontaine et al (2006), a worm model is generated using the geometry of planar curves. A nonlinear estimation of the model parameters is performed using a central difference Kalman filter (CDKF).…”

“…Some methods aim to track one or two worms at high magnification (Fontaine et al 2006;Geng et al 2004;Huang et al 2007), while others perform large-scale automated analysis of entire worm populations (Roussel et al 2007). In Geng et al (2004), the automatic tracking of a single C. elegans from a video sequence is demonstrated.…”

RNAi Methods and Screening: RNAi Based High-Throughput Genetic Interaction Screening

“…Some algorithms are being developed for similar but not identical images (Fontaine et al, 2006;Geng et al, 2004;Huang et al, 2006;O'Rourke et al, 2009). An algorithm for the automated analysis of images is being developed in our lab to streamline this process and provide quantitative results (White et al, 2010).…”