Metadata-Version: 2.1
Name: cellfinder
Version: 0.4.11rc3
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: adam.tyson@ucl.ac.uk
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).
         
        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:adam.tyson@ucl.ac.uk?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)
        
        
        ## 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
