STrack: A tool to Simply Track bacterial cells in microscopy time-lapse images

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Abstract Bacterial growth can be studied at the single cell-level through time-lapse microscopy imaging. Technical advances in microscopy lead to increasing image quality, which in turn allows to visualize larger areas of growth, containing more and more cells. In this context, the use of automated computational tools becomes essential. In this paper, we present STrack, a tool that allows to track cells in time-lapse images in a fast and efficient way. We compared it to three recently published tracking tools on images ranging over six different bacterial strains, and STrack showed to be the most consistent tracking tool, returning more than 80% of correct cell lineages on average. The python implementation of STrack, a docker structure, and a tutorial on how to download and use the tool can be found on the following github page: https://github.com/Helena-todd/STrack

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