PyUAT: An open-source Python framework for uncertainty-aware, efficient, and scalable model-driven cell tracking.

ABSTRACT: Tracking individual cells in live-cell imaging provides fundamental insights into phenotypic heterogeneity and cellular responses to environmental change. However, microbial cell tracking is particularly challenging, as cell growth is characterized by stochastic cell movements and frequent divisions, while time-lapses are recorded at limited frame rates to avoid counterfactual results. Here, we investigate how probabilistic Uncertainty-Aware Tracking (UAT), a paradigm based on statistical models of cell behavior, robustifies tracking quality under such challenging conditions. Using , the first open-source implementation of UAT, we systematically analyze the role of cell development models on tracking quality under increasing imaging intervals. Our results on a large 2D+t dataset demonstrat

SUBMITTER: Seiffarth J 

PROVIDER: S-EPMC12697953 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature