Segmentation finds the cells in one frame. The harder job is following each one through the next thousand — as it crawls, brightens, and above all divides, one cell becoming two. That temporal-linking problem is cell tracking, and it has its own quiet history. The classical answer was global optimization: the KTH tracker of Magnusson and Jaldén used the Viterbi algorithm to link whole tracks across an entire movie at once, handling mitosis, apoptosis, and cells wandering out of view. The Cell Tracking Challenge made progress objective — 21 algorithms on 13 datasets, with the honest verdict that ’none obtained fully correct solutions.’ Then deep learning moved in: TrackMate 7 wired modern segmentation into an open Fiji pipeline; DeepCell’s Caliban learned tracking-with-lineage from human-in-the-loop-labeled movies; and Trackastra replaced hand-tuned linking with a transformer that learns cell-to-cell associations and even handles division. It’s the temporal spine of every live-cell experiment the lab runs — and a field KTH helped build. But a track is a hypothesis about a cell’s identity over time: errors compound frame by frame, and the benchmark still isn’t solved.
