Accurate segmentation and tracking of single cells in complex 4D bacterial biofilms using machine learning

Abstract

Bacterial biofilms are complex 3-dimentional structures with substantial spatial and temporal heterogeneity at the single-cell level. Simultaneous multi-cell tracking in 3D is thus critical for analyzing single-cell behaviors, such as motility and metabolism, as well as lineage tracing in biofilms. Due to phototoxicity and photobleaching concerns, fluorescence images are often characterized by having low signal-to-background ratios (SBRs). High cell density, and large relative cell movements from frame to frame add additional challenges for accurate segmentation and tracking of living biofilms.