Segmentation and tracking of Pseudomonas aeruginosa for cell dynamics analysis in time-lapse images

Abstract

The swarming behavior of Pseudomonas aeruginosa is an important factor to the formation of bacteria biofilm which may cause infections in susceptible individuals. A key issue in studying the mechanism of P. aeruginosa swarming is to analyze the cell dynamics in time-lapse images. We propose a complete toolkit for producing quantitative results of cell dynamics analysis in time-lapse images of P. aeruginosa. The toolkit includes automatic cell segmentation and tracking, and optional tools for human interaction. Our proposed segmentation and tracking algorithms achieve considerable improvement over one of the best known open-source software for cell segmentation and tracking. A set of graphical interfaces is developed for human annotation, segmentation and tracking results inspection and correction, which are generic and can be combined with most other cell segmentation and tracking algorithms. Our toolkit is convenient for scientific researchers to use, even without knowledge of image processing, to perform cell dynamics analysis efficiently.