Tracking
The Tracking module links segmented cells across frames to create trajectories. This allows you to analyze cell motility, lineage, and dynamic behaviors.
Overview
After segmentation, individual cell detections exist independently in each frame. Tracking connects these detections across time to form trajectories, assigning a persistent identity to each cell. This is essential for any time-resolved analysis — measuring speed, detecting events such as division or death, and studying interactions between populations.
Available trackers
Celldetective integrates two tracking algorithms:
bTrack [1] (default) — a Bayesian tracker that uses Kalman filters and cell features to predict motion. It handles complex behaviors such as division and apoptosis, and is the recommended choice for crowded scenes.
trackpy — a Crocker–Grier particle tracker well-suited for simple Brownian motion.
Both trackers produce a table of cell positions, identities, and (optionally) morphological or intensity features per frame. Results are saved as a CSV file (trajectories_<population>.csv) in the output/tables folder of each position.
Post-processing
After tracking, optional post-processing can be applied to clean up results:
Filter out short tracks.
Interpolate gaps (missing detections within a track).
Extrapolate positions backwards or forwards to the movie boundaries.
For a full list of post-processing and tracker parameters, see the Tracking Settings Reference.
How-to guides
Task |
Guide |
|---|---|
Configure a tracker and run it on your data |
|
Correct a tracking error |