PyUAT: An open-source Python framework for uncertainty-aware, efficient, and scalable model-driven cell tracking - Johannes Seiffarth; Katharina Nöh (PLoS ONE, 2025)
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 demonstrate that model-driven cell tracking not only achieves higher accuracy at low frame rates, but also outperforms comparable methods in runtime efficiency. is available at https://github.com/JuBiotech/PyUAT , including example notebooks for immediate use in Google Colab.