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

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

Dimalis: A complete standalone pipeline to analyse prokaryotic cell growth from time-lapse imaging

“…We present and tested here an automated pipeline for segmentation and tracking of bacterial cell types, to facilitate analysis of time-lapse microscopy imaging and extraction of relevant growth kinetic data. The pipeline was combined mostly from existing individual software tools, notably BM3D (Djurović, 2016;Sheng, et al, 2014), Omnipose (Cutler, et al, 2022) and STrack (Todorov, et al, 2023), wrapped inside a Docker structure for consistency and ease of application. We minimize user input, restricting it to a single parameter setting for the denoising, and three parameters that cover the expected average cell diameter, the expected distance between cells for the tracking and the allowed division angle.…”

“…Dimalis is wrapped in a docker structure. The pipeline contains the BM3D image denoising tool (Djurović, 2016;Sheng, et al, 2014) (version 4.0.1), the Omnipose cell segmentation algorithm (Cutler, et al, 2022) (version 0.7.1), the STrack cell tracking algorithm (Todorov, et al, 2023) (version 4), and home-made python scripts that allow to extract cell features using the regionprops scikit-image module from python.…”

“…Consequently, we included Omnipose for cell segmentation in the Dimalis pipeline. For the cell tracking across time-lapse images we used our recently developed tracking tool STrack (Todorov, et al, 2023). The precision of STrack cell tracking in comparison to other tracking tools has been described in Ref (Todorov, et al, 2023).…”