{
    "info": {
        "author": "Filip Mroz",
        "author_email": "fafafft@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: C",
            "Programming Language :: C++",
            "Programming Language :: Cython",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Image Recognition"
        ],
        "description": "CellStar\r\n========\r\nversion 1.3.0\r\n\r\nIntroduction\r\n------------\r\nAutomatic tracking of cells in time-lapse microscopy is required to investigate a multitude of biological questions. To limit manipulations during cell line preparation and phototoxicity during imaging, brightfield imaging is often considered. Since the segmentation and tracking of cells in brightfield images is considered to be a difficult and complex task, a number of software solutions have been already developed.\r\n \r\nCellStar is one of such algorithms. It is optimized to segment and track images of budding yeast cells growing in monolayer (e.g. images from microfluidic chambers), however the algorithm can be also used to track other round objects (in brightfield as well as fluorescent images).\r\n\r\nThe important part of that solution is parameter fitting mechanism which allows to train and use CellStar for many different types of imagery.\r\n\r\nPlease visit our website http://www.cellstar-algorithm.org/ for more details.\r\n\r\nDistributions\r\n-------------\r\nThere are three ways of using CellStar:\r\n\r\n- python package https://github.com/Fafa87/cellstar\r\n\r\n- plugin for CellProfiler 2.2 http://cellstar-algorithm.strikingly.com/#download\r\n\r\n- matlab version for manual curation http://cellstar-algorithm.strikingly.com/#download\r\n\r\nThe plugin package includes not only the plugin itself but also examples of its usage to guide users on how to achieve best segmentation on a given type of imagery.\r\n\r\nWide range of example usages\r\n----------------------------\r\nDuring the testing phase of our plugin it turned out that combining parameter fitting and CellProfiler pipeline flow can result in a very flexible solution. In order to show that and also provide a quick starting point for users the `Official user guide <https://drive.google.com/file/d/0B3to8FwFxuTHNnJZbXRIdTdWTFE/view>`_ was prepared.\r\n\r\nIt contains the ready to use segmentation solution for a wide range of various imagery which includes:\r\n\r\n- complete pipeline description\r\n\r\n- method selection discussions\r\n\r\n- CellProfiler Analyst usage for advanced filtering\r\n\r\nThe pipelines listed in the document along with the actual imagery are available as a part of plugin version. Every case can be easily to recreate the results.\r\n\r\n.. image:: http://res.cloudinary.com/hrscywv4p/image/upload/c_limit,fl_lossy,h_1440,w_720,f_auto,q_auto/v1/92051/tiles_ytp2ac.jpg\r\n",
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        "keywords": "brightfield,yeast,segmentation",
        "license": "BSD",
        "maintainer": null,
        "maintainer_email": null,
        "name": "CellStar",
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        "summary": "Algorithm for round cells identification in the brightfield microscopy images.",
        "version": "1.3.0"
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