LapTrack
Project description
Features
Provides a robust particle tracking algorithm using the Linear Assignment Problem, with various cost functions for linking.
See the preprint and associated repository for the algorithm and parameter optimization by Ray-Tune.
Installation
You can install LapTrack via pip from PyPI:
$ pip install laptrack
Usage
Please see the Usage for details.
Contributing
Contributions are very welcome. To learn more, see the Contributor Guide.
License
Distributed under the terms of the The 3-Clause BSD License, LapTrack is free and open source software.
Issues
If you encounter any problems, please file an issue along with a detailed description.
Credits
This program implements a modified version of the algorithm in the K. Jaqaman et al. (2008).
Inspired by TrackMate a lot. See documentation for its detailed algorithm, the 2016 paper, and the 2021 paper.
The data in examples/interactive_example_data are generated by cropping images in 10.5281/zenodo.6087728, which is distributed with Creative Commons Attribution 4.0 International.
The data in examples/segmentation are generated by cropping and resizing images in https://osf.io/ysaq2/, which is distributed with Creative Commons Attribution 4.0 International. See 10.1038/sdata.2018.237 for details.
This project was generated from @cjolowicz’s Hypermodern Python Cookiecutter template.
Citation
If you use this program for your research, please cite it and help us build more.
@misc{fukai_2022,
title = {{{LapTrack}}: {{Linear}} Assignment Particle Tracking with Tunable Metrics},
shorttitle = {{{LapTrack}}},
author = {Fukai, Yohsuke T. and Kawaguchi, Kyogo},
year = {2022},
month = oct,
pages = {2022.10.05.511038},
publisher = {{bioRxiv}},
doi = {10.1101/2022.10.05.511038},
}
@misc{laptrack,
author = {Yohsuke T. Fukai},
title = {laptrack},
year = {2021},
url = {https://doi.org/10.5281/zenodo.5519537},
}
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