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

PLoS ONE, vol. 20, pp. e0337110

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 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.

Authors 2

1. Johannes Seiffarth Aachen Computational Systems Biotechnology

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany

   Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany

2. Katharina Nöh corresponding

   Forschungszentrum Jülich

   Affiliation as printed

   Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, Jülich, Germany