The problem — A live-cell experiment that cannot reliably link the same cell across hundreds of frames produces a track table that is, in effect, a table of coincidences. Motility statistics, signaling kinetics, and division timing all depend on frame-to-frame identity being correct. Errors compound: a single missed link breaks the lineage; a spurious merge assigns one cell's fate to another. Most pipeline failures here are invisible — the track table is populated and plausible-looking, and the error only surfaces when a biological claim does not replicate.
What it is / how it works — Live-cell imaging captures fluorescence time-lapse movies: multiple positions, multiple channels, hundreds to thousands of frames. The pipeline moves in two passes. First, each frame is segmented independently — Cellpose or a comparable model produces per-frame masks and confidence scores. Second, a tracker links those masks across time, building continuous trajectories. TrackMate, btrack, and ultrack all approach the linking step differently (LAP assignment, Bayesian graphical model, network-flow optimisation), but all of them expose the same failure surface: missed detections, cell–cell occlusion, and rapid mitosis.
Before tracking can be trusted, two pre-conditions must hold. Frame registration (pystackreg) corrects for stage drift so apparent motion is real motion, not instrument jitter. Illumination correction (BaSiCPy) removes flat-field variation that would otherwise be read as intensity change over time — a direct confound for any signaling readout.
QC in this modality is temporal: focus and photon budget must hold across the full run, not just in the first frame. Phototoxicity and photobleaching are silent degraders — the cell is responding to the measurement, and the signal is decaying. A movie-QC report flags both.
Where it breaks — Track integrity, not segmentation accuracy, is the quantity that matters for biology. Per-frame segmentation metrics do not predict linking performance — a model that holds Dice steady while producing fragmented or merged objects at confluent regions will generate systematically wrong lineages. Division events are particularly vulnerable: mitotic rounding changes cell morphology sharply, confusing the linker at precisely the moment the lineage branches. The discipline is to validate track-level outputs against labeled lineage benchmarks, not just per-frame overlap, and to report phototoxicity, bleaching decay, and positional drift as first-class QC outputs alongside the event timeline.
How we run it — the Live-Cell Tracking pipeline, stage by stage — the nine-stage imaging backbone reshaped for this modality, with the tools, sub-steps and deliverable at each stage.