PyUAT: Open-source Python framework for efficient and scalable cell tracking
This provides a tool for researchers studying microbial cell behavior, but it is incremental as it implements an existing uncertainty-aware tracking method in a new software package.
The authors tackled the challenge of tracking microbial cells in live-cell imaging by developing PyUAT, an open-source Python framework for uncertainty-aware tracking, which they demonstrated on a large 2D+t dataset with efficient performance.
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.