Cell tracking with accurate error prediction

Abstract

Cell tracking is an indispensable tool for studying development by time-lapse imaging. However, existing cell trackers cannot assign confidence to predicted tracks, which prohibits fully automated analysis without manual curation. We present a fundamental advance: an algorithm that combines neural networks with statistical physics to determine cell tracks with error probabilities for each step in the track. From these we can obtain error probabilities for any tracking feature, from cell cycles to lineage trees, that function like p-values in data interpretation. Our method greatly speeds up tracking analysis by limiting manual curation to rare low-confidence tracking steps. Importantly, it also enables fully-automated analysis by retaining only high-confidence track segments, which we demonstrate by analyzing cell cycles and differentiation events at scale, for thousands of cells in multiple intestinal organoids. Our approach brings cell dynamics-based organoid screening within reach, and enables transparent reporting of cell tracking results and associated scientific claims.