Metadata-Version: 2.1
Name: cellfinder-napari
Version: 0.0.19
Summary: Efficient cell detection in large images
Home-page: https://brainglobe.info/cellfinder
Author: Adam Tyson
Author-email: code@adamltyson.com
License: BSD-3-Clause
Project-URL: Source Code, https://github.com/brainglobe/cellfinder-napari
Project-URL: Bug Tracker, https://github.com/brainglobe/cellfinder-napari/issues
Project-URL: Documentation, https://docs.brainglobe.info/cellfinder-napari/
Project-URL: User Support, https://forum.image.sc/tag/brainglobe
Description: # cellfinder-napari
        
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        [![Website](https://img.shields.io/website?up_message=online&url=https%3A%2F%2Fcellfinder.info)](https://cellfinder.info)
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        ### Efficient cell detection in large images (e.g. whole mouse brain images)
        
        `cellfinder-napari` is a front-end to [cellfinder-core](https://github.com/brainglobe/cellfinder-core) to allow ease of use within the [napari](https://napari.org/index.html) multidimensional image viewer. For more details on this approach, please see [Tyson, Rousseau & Niedworok et al. (2021)](https://doi.org/10.1371/journal.pcbi.1009074). This algorithm can also be used within the original
        [cellfinder](https://github.com/brainglobe/cellfinder) software for
        whole-brain microscopy analysis.
        
        `cellfinder-napari`, `cellfinder` and `cellfinder-core` were developed by [Charly Rousseau](https://github.com/crousseau) and [Adam Tyson](https://github.com/adamltyson) in the [Margrie Lab](https://www.sainsburywellcome.org/web/groups/margrie-lab), based on previous work by [Christian Niedworok](https://github.com/cniedwor), generously supported by the [Sainsbury Wellcome Centre](https://www.sainsburywellcome.org/web/).
        
        ----
        ![raw](https://raw.githubusercontent.com/brainglobe/cellfinder-napari/master/resources/cellfinder-napari.gif)
        
        **Visualising detected cells in the cellfinder napari plugin**
        
        ----
        ## Instructions
        
        ### Installation
        Once you have [installed napari](https://napari.org/index.html#installation).
        You can install napari either through the napari plugin installation tool, or
        directly from PyPI with:
        ```bash
        pip install cellfinder-napari
        ```
        
        ### Usage
        Full documentation can be
        found [here](https://docs.brainglobe.info/cellfinder-napari).
        
        This software is at a very early stage, and was written with our data in mind.
        Over time we hope to support other data types/formats. If you have any
        questions or issues, please get in touch [on the forum](https://forum.image.sc/tag/brainglobe) or by
        [raising an issue](https://github.com/brainglobe/cellfinder-napari/issues).
        
        
        ---
        ## Illustration
        
        ### Introduction
        cellfinder takes a stitched, but otherwise raw dataset with at least
        two channels:
         * Background channel (i.e. autofluorescence)
         * Signal channel, the one with the cells to be detected:
        
        ![raw](https://raw.githubusercontent.com/brainglobe/cellfinder/master/resources/raw.png)
        **Raw coronal serial two-photon mouse brain image showing labelled cells**
        
        
        ### Cell candidate detection
        Classical image analysis (e.g. filters, thresholding) is used to find
        cell-like objects (with false positives):
        
        ![raw](https://raw.githubusercontent.com/brainglobe/cellfinder/master/resources/detect.png)
        **Candidate cells (including many artefacts)**
        
        
        ### Cell candidate classification
        A deep-learning network (ResNet) is used to classify cell candidates as true
        cells or artefacts:
        
        ![raw](https://raw.githubusercontent.com/brainglobe/cellfinder/master/resources/classify.png)
        **Cassified cell candidates. Yellow - cells, Blue - artefacts**
        
        ## Citing cellfinder
        
        If you find this plugin useful, and use it in your research, please cite the preprint outlining the cell detection algorithm:
        > Tyson, A. L., Rousseau, C. V., Niedworok, C. J., Keshavarzi, S., Tsitoura, C., Cossell, L., Strom, M. and Margrie, T. W. (2021) “A deep learning algorithm for 3D cell detection in whole mouse brain image datasets’ PLOS Computational Biology, 17(5), e1009074
        [https://doi.org/10.1371/journal.pcbi.1009074](https://doi.org/10.1371/journal.pcbi.1009074)
        
        
        **If you use this, or any other tools in the brainglobe suite, please
         [let us know](mailto:code@adamltyson.com?subject=cellfinder-napari), and
         we'd be happy to promote your paper/talk etc.**
        
        ---
        The BrainGlobe project is generously supported by the Sainsbury Wellcome Centre and the Institute of Neuroscience, Technical University of Munich, with funding from Wellcome, the Gatsby Charitable Foundation and the Munich Cluster for Systems Neurology - Synergy.
        
        <img src='https://brainglobe.info/images/logos_combined.png' width="550">
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Framework :: napari
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
