laptrack 0.7.0

LapTrack

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},
}