Tracking the swimming motions of C. elegans worms with applications in aging studies.
Quantitative analysis of the swimming motions of C. elegans worms are of critical importance for many gene-related studies on aging. However no automated methods are currently in use. We present a novel training-based method that automatically tracks and segments multiple swimming worms, in challenging imaging conditions. The position of each worm is predicted by comparing its latest motion with a set of previous observations, and then adjusted laterally and longitudinally to fit the image. After segmentation, a variety of measures can be used to assess the evolution of swimming patterns over time, allowing a quantitative comparison of worm populations over their lifetime. The complete software is being evaluated for mass processing in biology laboratories.
Restif
Christophe
C
Rutgers, the State University of New Jersey, USA. christophe.restif@centraliens.net
Metaxas
Dimitris
D
D000375:Aging / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D017173:Caenorhabditis elegans / Q000033:anatomy & histology* / Q000502:physiology*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018715:Microscopy, Video / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013550:Swimming / Q000502:physiology*