Tracking ======== .. _track: 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: * :term:`bTrack` [#]_ (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_.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 :ref:`Tracking Settings Reference `. How-to guides ------------- .. list-table:: :widths: 50 50 :header-rows: 1 * - Task - Guide * - Configure a tracker and run it on your data - :doc:`how-to ` * - Correct a tracking error - :doc:`how-to ` References ---------- .. [#] Ulicna, K., Vallardi, G., Charras, G. & Lowe, A. R. Automated Deep Lineage Tree Analysis Using a Bayesian Single Cell Tracking Approach. Frontiers in Computer Science 3, (2021).