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.

A semi-automated cell tracking protocol for quantitative analyses of neutrophil swarming to sterile and S. aureus contaminated bone implants in a mouse femur model

“…Each track took about a minute for the users to verify (across 60 frames). Previous studies proposed solutions to overcome this limitation by introducing specific rules for linking tracks when two cells collide or come close to each other [ 8 ]. However, these algorithms are computationally expensive, and these rules are specific to the cell type and imaging settings.…”

A semi-automated cell tracking protocol for quantitative analyses of neutrophil swarming to sterile andS. aureuscontaminated bone implants in a mouse femur model

“…Previous studies proposed solutions to overcome this limitation by introducing specific rules for linking tracks when two cells collide or come close to each other. (8) However, these algorithms are computationally expensive, and these rules are specific to the cell type and imaging settings. Future optimization focusing on connection rules and penalties will help address this issue.…”

“…Previous studies have reported diverse approaches to counting and tracking cells using TrackMate. (8)(9)(10)(11)(12) However, some of these tracking approaches utilize computationally expensive models or are restricted to specific operating systems. (12) The most apparent cellular architecture in the bone infection setting is the accumulation of bacteria and host immune cells over time.…”

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“…To quantify neutrophil swarming kinetics, TrackMate [5,6], an open-source, user-friendly plugin available within Fiji [7] is an attractive option that allows interactive cell tracking. Previous studies have reported diverse approaches to counting and tracking cells using TrackMate [8][9][10][11][12]. However, some of these tracking approaches utilize computationally expensive models or are restricted to specific operating systems [12].…”