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STrack: A Tool to Simply Track Bacterial Cells in Microscopy Time-Lapse Images
Helena Todorov1, Tania Miguel Trabajo1, Jan Roelof van der Meer1
1Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland.
Msphere
|March 20, 2023
Summary
STrack is a new computational tool for tracking bacterial cells in time-lapse microscopy images. It offers fast, efficient, and consistent cell lineage tracking, outperforming other tools with over 80% accuracy.
Area of Science:
- Microbiology
- Computational Biology
- Microscopy
Background:
- Bacterial growth studies benefit from single-cell analysis using time-lapse microscopy.
- Increasing image quality and size necessitate automated computational tools for efficient data analysis.
Purpose of the Study:
- To introduce STrack, a novel computational tool for rapid and efficient cell tracking in time-lapse microscopy images.
- To evaluate STrack's performance against existing tracking tools across diverse bacterial strains and morphologies.
Main Methods:
- Developed STrack, a Python-based tool for automated cell tracking.
- Compared STrack with three other tracking tools using image datasets from six bacterial strains.
- Validated tracking accuracy against manually annotated ground-truth data.
Main Results:
- STrack demonstrated superior consistency compared to other tools.
- Achieved over 80% accuracy in correct cell lineage tracking on average.
- Provided fast run times and generated cell tables for subsequent lineage analysis.
Conclusions:
- STrack is a highly effective and simplified tool for bacterial cell tracking, suitable for non-specialists.
- The open-source availability and ease of use facilitate wider adoption and further development in automated image analysis.
