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

Cell Rep Methods. 2023 Nov 20;3(11):100636. doi: 10.1016/j.crmeth.2023.100636. Epub 2023 Nov 13.

Abstract

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.

Keywords: CP: Imaging; cell tracking correction; high-throughput imaging; lineage tracking; live-cell imaging; machine-learning-based cell segmentation; real-time cell tracking.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Cell Tracking / methods
  • Image Processing, Computer-Assisted* / methods
  • Microscopy