Instant processing of large-scale image data with FACT, a real-time cell segmentation and tracking algorithm

Summary: Quantifying cellular characteristics from a large heterogeneous population is essential to identify rare, disease-driving cells. A recent development in the combination of high-throughput screening microscopy with single-cell profiling provides an unprecedented opportunity to decipher disease-driving phenotypes. Accurately and instantly processing large amounts of image data, however, remains a technical challenge when an analysis output is required minutes after data acquisition. Here, we present fast and accurate real-time cell tracking (FACT). FACT can segment ∼20,000 cells in an average of 2.5 s (1.9–93.5 times faster than the state of the art). It can export quantifiable features minutes after data acquisition (independent of the number of acquired image frames) with an average of 90%–96% precision. We apply FACT to identify directionally migrating glioblastoma cells with 96% precision and irregular cell lineages from a 24 h movie with an average F1 score of 0.91. Motivation: The evolution of advanced volumetric fluorescence and high-throughput screening microscopy techniques has ushered in a new era of data-intensive cellular imaging. These advancements have triggered the development of computational algorithms for extracting biological insights from multidimensional image datasets. Various computational methods have been proposed to deal with object segmenting/tracking problems emerging from high-throughput data (>20,000 objects per field of view). However, challenges remain in capturing intricate cellular behaviors from densely populated cultures: cells that are prone to overlap make high-throughput cell segmentation difficult, resulting in a low cell tracking accuracy; large datasets are hard to process in real time, meaning existing computational pipelines can typically not provide instant (