Abstract
In vivo calcium imaging enables simultaneous recording of large neuronal ensembles engaged in complex operations. Many experiments require monitoring and identification of cell populations across multiple sessions. Population cell tracking across multiple sessions is complicated by non-rigid transformations induced by cell movement and imaging field shifts. We introduce SCOUT (Single-Cell SpatiOtemporal LongitUdinal Tracking), a fast, robust cell tracking method utilizing multiple cell-cell similarity metrics, probabilistic inference, and an adaptive clustering methodology to perform cell identification across multiple sessions. By comparing SCOUT with current popular cell tracking algorithms on simulated, 1-photon and 2-photon recordings, we empirically show this approach improves cell tracking quality, particularly when recordings exhibit spatial footprint movement between sessions or poor neural extraction quality.
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