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Indeed, microbiologists are confronted with numerous algorithms that must be carefully chosen and parameterized to achieve optimal tracking for their experiments. We present an automated methodology to determine optimal tracking configurations for microbiological applications. It is based on TrackMate 8, a novel version of the TrackMate Fiji plugin extended with microbiology\u2010specific tools. Our approach systematically evaluates algorithm\u2010parameter combinations optimizing biologically relevant metrics (e.g., cell\u2010cycle accuracy, bacterial morphology) and includes: (1) integration of deep\u2010learning algorithms (Omnipose, YOLO, Trackastra) adequate for bacterial images in TrackMate; (2) a TrackMate\u2010Helper extension for parameter optimization; and (3) a tracking and segmentation editor for tracking ground\u2010truth generation. We demonstrate the effectiveness of the methodology on two use cases showing its adaptability to diverse experimental conditions. This methodology enables microbiologists with a widely applicable, automated framework to optimize tracking pipelines, facilitating quantitative analysis in bacterial imaging. \u00a9 2026 Wiley Periodicals LLC.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Basic Protocol 1<\/jats:bold>\n                    : Impact of the respiratory chain in the growth and morphology of\n                    <jats:italic>Escherichia coli<\/jats:italic>\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Basic Protocol 2<\/jats:bold>\n                    : The impact of mutations on the motility of\n                    <jats:italic>Helicobacter pylori<\/jats:italic>\n                  <\/jats:p>","DOI":"10.1002\/cpz1.70369","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T15:10:43Z","timestamp":1779117043000},"source":"Crossref","is-referenced-by-count":1,"title":["Automated Optimization of Bacterial 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