Automated Optimization of Bacterial Tracking Pipelines With TrackMate 8

Area of Science:

• Microbiology
• Bioimaging
• Computational Biology

Background:

• Quantitative analysis of bacterial dynamics using time-lapse microscopy relies on effective tracking algorithms.
• Selecting and optimizing these algorithms for specific experiments presents a significant challenge for microbiologists.

Purpose of the Study:

• To present an automated methodology for determining optimal tracking configurations in microbiological applications.
• To enhance the TrackMate Fiji plugin with microbiology-specific tools for improved bacterial image analysis.

Main Methods:

• The methodology is based on TrackMate 8, integrating deep-learning algorithms (Omnipose, YOLO, Trackastra) suitable for bacterial images.
• It includes a TrackMate-Helper extension for parameter optimization and a tracking/segmentation editor for ground-truth generation.
• The approach systematically evaluates algorithm-parameter combinations based on biologically relevant metrics like cell-cycle accuracy and bacterial morphology.

Main Results:

• Demonstrated the effectiveness and adaptability of the methodology across diverse experimental conditions using two use cases.
• Successfully integrated advanced deep-learning models into the tracking pipeline for enhanced segmentation and tracking accuracy.
• Enabled systematic optimization of tracking parameters, leading to improved biologically relevant metrics.

Conclusions:

• The developed methodology provides microbiologists with a widely applicable and automated framework for optimizing bacterial tracking pipelines.
• Facilitates more accurate and efficient quantitative analysis of bacterial dynamics in microscopy images.
• Advances the field of microbial imaging by streamlining complex data analysis processes.