STracking

STracking provides a modular Python framework for building and standardizing particle tracking pipelines to analyze dynamic processes such as vesicle transport in 2D+t and 3D+t microscopy images.

Key Features:

• Modular architecture: Components for particle dynamics analysis are organized to enable modularity and interoperability among filtering, detection, and tracking modules.
• Integrated computational tasks: Filtering, detection, and tracking steps are combined into a cohesive workflow.
• Deep learning segmentation integration: Supports integration with deep learning-based segmentation tools to link segmentation, detection, and tracking algorithms.
• Trajectory quality assessment: Provides mechanisms for assessing the quality of estimated particle trajectories.
• 2D+t and 3D+t image support: Designed to operate on two-dimensional plus time and three-dimensional plus time microscopy image data.
• Reproducibility and standardization: Standardizes pipeline construction to improve reproducibility of particle tracking analyses across studies.

Scientific Applications:

• Vesicle transport analysis: Analysis of vesicle transport dynamics within and outside cells using time-lapse and volumetric microscopy.
• Particle dynamics studies: Quantitative study of particle behavior in 2D+t and 3D+t microscopy sequences.
• Pipeline standardization: Construction and comparison of standardized particle tracking pipelines to enable reproducible analyses across experiments.

Methodology:

Computational steps explicitly include filtering, detection, and tracking of particles and integration with deep learning-based segmentation methods within a modular, interoperable pipeline.