Metadata-Version: 2.1
Name: cellfinder-napari
Version: 0.0.5
Summary: Efficient cell detection in large images
Home-page: https://cellfinder.info
Author: Adam Tyson
Author-email: code@adamltyson.com
License: GPL-3.0
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-plugin
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)
        [![Twitter](https://img.shields.io/twitter/follow/findingcells?style=social)](https://twitter.com/findingcells)
        
        
        
        ### Efficient cell detection in large images (e.g. whole mouse brain images)
        
        This package implements the cell detection algorithm from 
        [Tyson, Rousseau & Niedworok et al. (2021)](https://www.biorxiv.org/content/10.1101/2020.10.21.348771v2) 
        for [napari](https://napari.org/index.html), based on the 
        [cellfinder-core](https://github.com/brainglobe/cellfinder-core) package.
        
        This algorithm can also be used within the original 
        [cellfinder](https://github.com/brainglobe/cellfinder) software for 
        whole-brain microscopy analysis.
        
        ----
        ![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-plugin). 
         
        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 by 
        [email](mailto:code@adamltyson.com?subject=cellfinder-napari), 
        [gitter](https://gitter.im/BrainGlobe/cellfinder) or by 
        [raising an issue](https://github.com/brainglobe/cellfinder-napari/issues).
        
        
        ---
        ## Illustration
        
        ### Introduction
        cellfinder takes a stitched, but otherwise raw whole-brain 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’ bioRxiv, [doi.org/10.1101/2020.10.21.348771](https://doi.org/10.1101/2020.10.21.348771)
        
        
        **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.**
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Framework :: napari
Classifier: Topic :: Software Development :: Testing
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: BSD License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
