It will be of interest to the community of both end-users and developers of time-lapse microscopy software. The presented software simplifies quantitative analysis of time-lapse movies overall and is currently the only available software for the analysis of mycobacteria-like cells. We illustrate the power of the visualization tool by showing heterogeneity of both icl expression and cell growth atop of a lineage tree. We tested BactImAS on time-lapse movies of Mycobacterium smegmatis and achieved at least 10-fold reduction of processing time compared to manual analysis. Finally, the BactImAS can generate configurable lineage tree visualizations and export data as CSV files. Measurements are extracted from images in a configurable, automated fashion and an SQLite database is used to store, retrieve, and exchange all acquired data. The program segments and tracks cells using a newly-developed algorithm designed for movies with difficult-to-segment cells that exhibit small frame-to-frame differences. BactImAS uses a semi-automated approach where the user defines initial cells, identifies cell division events, and, if necessary, manually corrects cell segmentation with the help of user-friendly GUI and incorporated ImageJ application. The software is designed for extracting and visualizing quantitative data from bacterial time-lapse movies. Here we describe BactImAS – a modular, multi-platform, open-source, Java-based software delivered both as a standalone program and as a plugin for Icy. ![]() Unfortunately, many organisms of interest have these characteristics, and analyzing their image sequences has been limited to time consuming manual processing. These programs, mostly MATLAB-based, fail for microbes with irregular shape, indistinct cell division sites, or that grow in closely packed microcolonies. The software available to date for analyzing image sequences from time-lapse microscopy works only for certain bacteria and under limited conditions.
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