Python Uncertainty-Aware Tracking (PyUAT)
Efficient open-source Python implementation of the Uncertainty-Aware Tracking approach: https://doi.org/10.1093/bioinformatics/bty776
Usage
You can either use our colab notebooks for examples for cell tracking or install and run the examples on your computer locally. We recommend the usage of Linux ubuntu for a local installation.
Basic Example
We provide a basic example for using the tracking configurations described in our paper.
Customize cell models
We provide an example showing the integration of a new custom model (in that case cell age) into the tracking configuration.
Local Installation
Install PyUAT from pypi
pip install uatrack
Developer Installation
git clone https://github.com/JuBiotech/PyUAT
cd PyUAT
pip install -e .
Local usage
Try our example_simple.ipynb notebook to get started with the tracking.
Data Availability
We utilize the publicly available Tracking one-in-a-million dataset introduced by Seiffarth et al. at the ECCV 2024.
1 maintainer
Metadata
1 maintainer
Release files for uatrack 0.0.5
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