{"id":20070180,"url":"https://github.com/quantumjot/btrack","last_synced_at":"2025-12-12T01:02:13.546Z","repository":{"id":37396975,"uuid":"101179642","full_name":"quantumjot/btrack","owner":"quantumjot","description":"Bayesian multi-object 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Bayesian Tracker (btrack) 🔬💻\n\n`btrack` is a Python library for multi object tracking, used to reconstruct trajectories in crowded fields.\nHere, we use a probabilistic network of information to perform the trajectory linking.\nThis method uses spatial information as well as appearance information for track linking.\n\nThe tracking algorithm assembles reliable sections of track that do not contain splitting events (tracklets).\nEach new tracklet initiates a probabilistic model, and utilises this to predict future states (and error in states) of each of the objects in the field of view.\nWe assign new observations to the growing tracklets (linking) by evaluating the posterior probability of each potential linkage from a Bayesian belief matrix for all possible linkages.\n\nThe tracklets are then assembled into tracks by using multiple hypothesis testing and integer programming to identify a globally optimal solution.\nThe likelihood of each hypothesis is calculated for some or all of the tracklets based on heuristics.\nThe global solution identifies a sequence of high-likelihood hypotheses that accounts for all observations.\n\nWe developed `btrack` for cell tracking in time-lapse microscopy data.\n\n## Installation\n\n`btrack` has been tested with ![Python](https://img.shields.io/pypi/pyversions/btrack) (Python 3.9-3.13)\non `x86_64` `macos\u003e=11`, `ubuntu\u003e=20.04` and `windows\u003e=10.0.17763`.\nNote that `btrack\u003c=0.5.0` was built against earlier versions of\n[Eigen](https://eigen.tuxfamily.org) which used `C++11`, as of `btrack\u003e=0.5.1`\nit is now built against `C++17`.\n\n### Installing the latest stable version\n\n```sh\npip install btrack\n```\n\n## Usage examples\n\nVisit [btrack documentation](https://btrack.readthedocs.io) to learn how to use it and see other examples.\n\n### Cell tracking in time-lapse imaging data\n\nWe provide integration with Napari, including a plugin for graph visualization, [arboretum](https://btrack.readthedocs.io/en/latest/user_guide/napari.html).\n\n\n[![CellTracking](http://lowe.cs.ucl.ac.uk/images/youtube.png)](https://youtu.be/EjqluvrJGCg)  \n*Video of tracking, showing automatic lineage determination*\n\n\n\u003cimg src=\"https://user-images.githubusercontent.com/8217795/225356392-6eb4b68c-eda5-4b96-af50-76930fa45e9d.png\" width=\"700\" /\u003e\n\n\n---\n\n## Development\n\nThe tracker and hypothesis engine are mostly written in C++ with a Python wrapper.\nIf you would like to contribute to btrack, you will need to install the latest version from GitHub. Follow the [instructions on our developer guide](https://btrack.readthedocs.io/en/latest/dev_guide).\n\n\n---\n### Citation\n\nMore details of how this type of tracking approach can be applied to tracking cells in time-lapse microscopy data can be found in the following publications:\n\n**Automated deep lineage tree analysis using a Bayesian single cell tracking approach**  \nUlicna K, Vallardi G, Charras G and Lowe AR.  \n*Front in Comp Sci* (2021)  \n[![doi:10.3389/fcomp.2021.734559](https://img.shields.io/badge/doi-10.3389%2Ffcomp.2021.734559-blue)](https://doi.org/10.3389/fcomp.2021.734559)\n\n\n**Local cellular neighbourhood controls proliferation in cell competition**  \nBove A, Gradeci D, Fujita Y, Banerjee S, Charras G and Lowe AR.  \n*Mol. Biol. Cell* (2017)  \n[![doi:10.1091/mbc.E17-06-0368](https://img.shields.io/badge/doi-10.1091%2Fmbc.E17--06--0368-blue)](https://doi.org/10.1091/mbc.E17-06-0368)\n\n```\n@ARTICLE {10.3389/fcomp.2021.734559,\n   AUTHOR = {Ulicna, Kristina and Vallardi, Giulia and Charras, Guillaume and Lowe, Alan R.},\n   TITLE = {Automated Deep Lineage Tree Analysis Using a Bayesian Single Cell Tracking Approach},\n   JOURNAL = {Frontiers in Computer Science},\n   VOLUME = {3},\n   PAGES = {92},\n   YEAR = {2021},\n   URL = {https://www.frontiersin.org/article/10.3389/fcomp.2021.734559},\n   DOI = {10.3389/fcomp.2021.734559},\n   ISSN = {2624-9898}\n}\n```\n\n```\n@ARTICLE {Bove07112017,\n  author = {Bove, Anna and Gradeci, Daniel and Fujita, Yasuyuki and Banerjee,\n    Shiladitya and Charras, Guillaume and Lowe, Alan R.},\n  title = {Local cellular neighborhood controls proliferation in cell competition},\n  volume = {28},\n  number = {23},\n  pages = {3215-3228},\n  year = {2017},\n  doi = {10.1091/mbc.E17-06-0368},\n  URL = {http://www.molbiolcell.org/content/28/23/3215.abstract},\n  eprint = {http://www.molbiolcell.org/content/28/23/3215.full.pdf+html},\n  journal = {Molecular Biology of the Cell}\n}\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantumjot%2Fbtrack","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquantumjot%2Fbtrack","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantumjot%2Fbtrack/lists"}