{"name":"btrack","display_name":"btrack","visibility":"public","icon":"","categories":[],"schema_version":"0.1.0","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"btrack.read_btrack","title":"Read btrack files","python_name":"btrack.napari.reader:get_reader","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"btrack.write_hdf","title":"Export Tracks to HDF","python_name":"btrack.napari.writer:export_to_hdf","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"btrack.track","title":"Create Track","python_name":"btrack.napari.main:create_btrack_widget","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":[{"command":"btrack.read_btrack","filename_patterns":["*.h5","*.hdf","*.hdf5"],"accepts_directories":false}],"writers":[{"command":"btrack.write_hdf","layer_types":["tracks"],"filename_extensions":[".h5",".hdf",".hdf5"],"display_name":""}],"widgets":[{"command":"btrack.track","display_name":"Track","autogenerate":false}],"sample_data":null,"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.1","name":"btrack","version":"0.6.5","dynamic":null,"platform":null,"supported_platform":null,"summary":"A framework for Bayesian multi-object tracking","description":"[](https://pypi.org/project/btrack)\n[](https://pepy.tech/project/btrack)\n[](https://github.com/psf/black)\n[](https://github.com/quantumjot/btrack/actions/workflows/test.yml)\n[](https://github.com/pre-commit/pre-commit)\n[](https://btrack.readthedocs.io/en/latest/?badge=latest)\n[](https://codecov.io/gh/quantumjot/btrack)\n\n\n\n# 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 \non `x86_64` `macos>=11`, `ubuntu>=20.04` and `windows>=10.0.17763`.\nNote that `btrack<=0.5.0` was built against earlier version of\n[Eigen](https://eigen.tuxfamily.org) which used `C++=11`, as of `btrack==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\n We provide integration with Napari, including a plugin for graph visualization, [arboretum](https://btrack.readthedocs.io/en/latest/user_guide/napari.html).\n\n\n[](https://youtu.be/EjqluvrJGCg) \n*Video of tracking, showing automatic lineage determination*\n\n\n![](\"https://user-images.githubusercontent.com/8217795/225356392-6eb4b68c-eda5-4b96-af50-76930fa45e9d.png\")
\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[](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[](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","description_content_type":"text/markdown","keywords":null,"home_page":null,"download_url":null,"author":null,"author_email":"\"Alan R. Lowe\" ","maintainer":null,"maintainer_email":null,"license":"MIT License\n\nCopyright (c) 2017 Alan R. Lowe (quantumjot)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of\nthis software and associated documentation files (the \"Software\"), to deal in\nthe Software without restriction, including without limitation the rights to\nuse, copy, modify, merge, publish, distribute, sublicense, and/or sell copies\nof the Software, and to permit persons to whom the Software is furnished to do\nso, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n","classifier":["Framework :: napari","Intended Audience :: Science/Research","License :: OSI Approved :: MIT License","Operating System :: OS Independent","Programming Language :: C++","Programming Language :: Python :: 3","Programming Language :: Python :: 3 :: Only","Programming Language :: Python :: 3.9","Programming Language :: Python :: 3.10","Programming Language :: Python :: 3.11","Topic :: Scientific/Engineering :: Bio-Informatics","Topic :: Scientific/Engineering :: Image Recognition","Topic :: Scientific/Engineering :: Visualization"],"requires_dist":["cvxopt >=1.3.1","h5py >=2.10.0","numpy >=1.17.3","pandas >=2.0.3","pooch >=1.0.0","pydantic <2","scikit-image >=0.16.2","scipy >=1.3.1","tqdm >=4.65.0","black ; extra == 'dev'","pre-commit ; extra == 'dev'","ruff ; extra == 'dev'","numpydoc ; extra == 'docs'","pytz ; extra == 'docs'","sphinx ; extra == 'docs'","sphinx-automodapi ; extra == 'docs'","sphinx-panels ; extra == 'docs'","sphinx-rtd-theme ; extra == 'docs'","magicgui >=0.5.0 ; extra == 'napari'","napari-plugin-engine >=0.1.4 ; extra == 'napari'","napari >=0.4.16 ; extra == 'napari'","qtpy ; extra == 'napari'"],"requires_python":">=3.9","requires_external":null,"project_url":["bugtracker, https://github.com/quantumjot/btrack/issues","documentation, https://btrack.readthedocs.io","homepage, https://github.com/quantumjot/btrack","usersupport, https://github.com/quantumjot/btrack/discussions"],"provides_extra":["dev","docs","napari"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}