PyUAT高效追踪微生物细胞，解决低帧率下的跟踪难题
PyUAT: Open-source Python framework for efficient and scalable cell tracking
- 基于统计模型预测细胞关联，实现不确定性感知追踪
- 在2D+t大规模数据集上验证，追踪准确率显著提升
- 开源模块化设计，适合生物图像分析研究者快速上手
活细胞成像中追踪单个细胞可提供关键洞察，对研究表型异质性、环境变化或应激反应至关重要。微生物细胞追踪因细胞随机运动和频繁分裂，且受限于低帧率以避免反事实结果，仍具挑战性。一种有前景的解决方案是不确定性感知追踪（UAT），利用校准过的统计模型预测可能的细胞关联。本文提出PyUAT，一个高效、模块化的Python实现框架，用于时间序列成像中的微生物细胞追踪。我们在大型2D+t数据集上展示了其性能，并研究了模块化生物模型与成像间隔对追踪效果的影响。PyUAT开源，项目地址为https://github.com/JuBiotech/PyUAT，包含可直接在Google Colab运行的示例笔记本。
原文摘要 · Abstract (English)
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.
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