Metadata-Version: 2.1
Name: cellfinder
Version: 0.4.12
Summary: Automated 3D cell detection and registration of whole-brain images
Home-page: https://cellfinder.info
Author: Adam Tyson, Christian Niedworok, Charly Rousseau
Author-email: code@adamltyson.com
License: UNKNOWN
Project-URL: Source Code, https://github.com/brainglobe/cellfinder
Project-URL: Bug Tracker, https://github.com/brainglobe/cellfinder/issues
Project-URL: Documentation, https://docs.brainglobe.info/cellfinder
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        # Cellfinder
        Whole-brain cell detection, registration and analysis.
        
        ---
        
        
        Cellfinder is a collection of tools from the 
        [Margrie Lab](https://www.sainsburywellcome.org/web/groups/margrie-lab) and
         others at the [Sainsbury Wellcome Centre](https://www.sainsburywellcome.org/web/), [UCL](https://www.ucl.ac.uk/)
         for the analysis of whole-brain imaging data such as 
         [serial-section imaging](https://sainsburywellcomecentre.github.io/OpenSerialSection/)
         and lightsheet imaging in cleared tissue.
         
         The aim is to provide a single solution for:
         
         * Cell detection (initial cell candidate detection and refinement using 
         deep learning).
         * Atlas registration (using [brainreg](https://github.com/brainglobe/brainreg))
         * Analysis of cell positions in a common space
         
        Installation is with 
        `pip install cellfinder`.
        
        Basic usage:
        ```bash
        cellfinder -s signal_images -b background_images -o output_dir --metadata metadata
        ```
        Full documentation can be 
        found [here](https://docs.brainglobe.info/cellfinder). In particular, please 
        see the 
        [data requirements](https://docs.brainglobe.info/cellfinder/user-guide/data-requirements).
         
        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), 
        [gitter](https://gitter.im/BrainGlobe/cellfinder) or by 
        [raising an issue](https://github.com/brainglobe/cellfinder/issues/new/choose).
        
        
        ---
        ## 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**
        
        ### Registration and segmentation (brainreg)
        Using [brainreg](https://github.com/brainglobe/brainreg), 
        cellfinder aligns a template brain and atlas annotations (e.g. 
        the Allen Reference Atlas, ARA) to the sample allowing detected cells to be assigned 
        a brain region.
        
        This transformation can be inverted, allowing detected cells to be
        transformed to a standard anatomical space.
        
        ![raw](https://raw.githubusercontent.com/brainglobe/cellfinder/master/resources/register.png)
        **ARA overlaid on sample image**
        
        ### Analysis of cell positions in a common anatomical space
        Registration to a template allows for powerful group-level analysis of cellular
        disributions. *(Example to come)*
        
        ## Examples
        *(more to come)*
        
        ### Tracing of inputs to retrosplenial cortex (RSP)
        Input cell somas detected by cellfinder, aligned to the Allen Reference Atlas, 
        and visualised in [brainrender](https://github.com/brancolab/brainrender) along 
        with RSP.
        
        ![brainrender](https://raw.githubusercontent.com/brainglobe/cellfinder/master/resources/brainrender.png)
        
        Data courtesy of Sepiedeh Keshavarzi and Chryssanthi Tsitoura. [Details here](https://www.youtube.com/watch?v=pMHP0o-KsoQ)
        
        ## Visualisation
        
        cellfinder comes with a plugin ([brainglobe-napari-io](https://github.com/brainglobe/brainglobe-napari-io)) for [napari](https://github.com/napari/napari) to view your data
        
        #### Usage
        * Open napari (however you normally do it, but typically just type `napari` into your terminal, or click on your desktop icon)
        
        #### Load cellfinder XML file
        * Load your raw data (drag and drop the data directories into napari, one at a time)
        * Drag and drop your cellfinder XML file (e.g. `cell_classification.xml`) into napari.
        
        #### Load cellfinder directory
        * Load your raw data (drag and drop the data directories into napari, one at a time)
        * Drag and drop your cellfinder output directory into napari.
        
        The plugin will then load your detected cells (in yellow) and the rejected cell 
        candidates (in blue). If you carried out registration, then these results will be 
        overlaid (similarly to the loading brainreg data, but transformed to the 
        coordinate space of your raw data).
        
        ![load_data](https://raw.githubusercontent.com/brainglobe/brainglobe-napari-io/master/resources/load_data.gif)
        **Loading raw data**
        
        ![load_data](https://raw.githubusercontent.com/brainglobe/brainglobe-napari-io/master/resources/load_results.gif)
        **Loading cellfinder results**
        
        ## Citing cellfinder
        
        If you find cellfinder 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 any of the image registration functions in cellfinder, please also cite [brainreg](https://github.com/brainglobe/brainreg#citing-brainreg).
        
        **If you use this, or any other tools in the brainglobe suite, please
         [let us know](mailto:adam.tyson@ucl.ac.uk?subject=cellfinder), and 
         we'd be happy to promote your paper/talk etc.**
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows :: Windows 10
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
