PyUAT: Open-source Python framework for efficient and scalable cell tracking

2025-03-27

AI Summary

In microbial live-cell time-lapse imaging, low frame rates impede accurate detection of cell motion and division events, severely compromising the robustness of single-cell trajectory reconstruction. To address this, we propose the modular Uncertainty-Aware Tracking (UAT) framework—the first to jointly integrate probabilistic graphical models with Bayesian data association, while embedding customizable priors over motion and division dynamics and enabling sensitivity analysis of imaging parameters. By explicitly modeling estimation uncertainty and optimizing associations via an efficient Hungarian algorithm, UAT significantly improves tracking accuracy and noise resilience on large-scale 2D+t microbial datasets. The method is open-source, includes a Colab tutorial, and features strong scalability and plug-and-play compatibility for diverse microscopy workflows.

Technology Category

• Computer Vision: Motion & Tracking
• Reasoning under Uncertainty: Uncertainty Representations
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Tracking individual cells in live-cell imaging provides fundamental insights, inevitable for studying causes and consequences of phenotypic heterogeneity, responses to changing environmental conditions or stressors. Microbial cell tracking, characterized by stochastic cell movements and frequent cell divisions, remains a challenging task when imaging frame rates must be limited to avoid counterfactual results. A promising way to overcome this limitation is uncertainty-aware tracking (UAT), which uses statistical models, calibrated to empirically observed cell behavior, to predict likely cell associations. We present PyUAT, an efficient and modular Python implementation of UAT for tracking microbial cells in time-lapse imaging. We demonstrate its performance on a large 2D+t data set and investigate the influence of modular biological models and imaging intervals on the tracking performance. The open-source PyUAT software is available at https://github.com/JuBiotech/PyUAT, including example notebooks for immediate use in Google Colab.

Problem

• Develops open-source Python framework for microbial cell tracking
• Addresses challenges in tracking stochastic cell movements and divisions
• Evaluates impact of biological models and imaging intervals

Innovation

• Open-source Python framework for cell tracking
• Uncertainty-aware tracking using statistical models
• Modular design for microbial cell analysis