Fast and Accurate Cell Tracking: a real-time cell segmentation and tracking algorithm to instantly export quantifiable cellular characteristics from large scale image data

Abstract

Quantitative characterizations of cellular dynamics and features of individual cells from a large heterogenous population is essential to identify rare, disease-driving cells, which often exhibit aberrant cellular behaviors like abnormal division, aggressive migration or irregular phylogenetic cell lineages. A recent development in the combination of high-throughput screening microscopy with single cell profiling provides an unprecedented opportunity to decipher the underlying mechanisms of disease-driving phenotypes observed under a microscope. However, accurately and instantly processing large amounts of image data like longitudinal time lapse movies remains a technical challenge when an immediate analysis output (in minutes) of quantitative characterizations is required after data acquisition. Here we present a Fast and Accurate real-time Cell Tracking (FACT) algorithm, which combines GPU-based, ground truth-assisted trainable Weka segmentation and real-time Gaussian mixture model-based cell linking. FACT also implements an automatic cell track correction function to improve the tracking accuracy. With FACT, we can segment [~]20,000 cells in 2 seconds ([~]4.5-27.5 times faster than state-of-the-art), and can export quantifiable features from the cell tracking results minutes after data acquisition (independent of the number of acquired image frames) with average 90-95% tracking precision. Such performance is not feasible with state-of-the-art cell tracking algorithms. We applied FACT to real-time identify directionally migrating glioblastoma cells with 96% precision and to identify rare, irregular cell lineages in a population of [~]10,000 cells from a 24hr-time lapse movie with an average 91% F1 score, results from both were exported instantly, mere minutes after image acquisition.