Metadata-Version: 2.1
Name: cellfinder
Version: 0.3.14rc2
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/SainsburyWellcomeCentre/cellfinder
Project-URL: Bug Tracker, https://github.com/SainsburyWellcomeCentre/cellfinder/issues
Project-URL: Documentation, https://docs.cellfinder.info
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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/)
         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 [amap](https://github.com/SainsburyWellcomeCentre/amap-python))
         * 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.cellfinder.info/).
         
        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/cellfinder/community) or by 
        [raising an issue](https://github.com/SainsburyWellcomeCentre/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/SainsburyWellcomeCentre/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/SainsburyWellcomeCentre/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/SainsburyWellcomeCentre/cellfinder/master/resources/classify.png)
        **Cassified cell candidates. Yellow - cells, Blue - artefacts**
        
        ### Registration and segmentation (amap)
        Using [amap](https://github.com/SainsburyWellcomeCentre/amap-python), 
        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/SainsburyWellcomeCentre/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/SainsburyWellcomeCentre/cellfinder/master/resources/brainrender.png)
        
        Data courtesy of Sepiedeh Keshavarzi and Chryssanthi Tsitoura. [Details here](https://www.youtube.com/watch?v=pMHP0o-KsoQ)
        
        
        ## Additional tools
        cellfinder is packaged with 
        [neuro](https://github.com/sainsburywellcomecentre/neuro) which provides 
        additional tools for the analysis of visualisation of whole-brain imaging data.
        
        #### Heatmaps of detected cells:
        ![heatmap](https://raw.githubusercontent.com/SainsburyWellcomeCentre/cellfinder/master/resources/heatmap.png)
        
        #### Mapping non-cellular volumes in standard space:
        ![injection](https://raw.githubusercontent.com/SainsburyWellcomeCentre/cellfinder/master/resources/injection.png)
        **Virus injection site within the superior colliculus.**
        *(Data courtesy of [@FedeClaudi](https://github.com/fedeclaudi) and 
        [brainrender](https://github.com/brancolab/brainrender))*
        
        ## Citing cellfinder
        
        If you find cellfinder useful, and use it in your research, please cite this repository:
        
        > Adam L. Tyson, Charly V. Rousseau, Christian J. Niedworok and Troy W. Margrie (2020). cellfinder: automated 3D cell detection and registration of whole-brain images. [doi:10.5281/zenodo.3665329](http://doi.org/10.5281/zenodo.3665329)
        
        If you use any of the image registration functions in cellfinder, please also cite [amap](https://github.com/SainsburyWellcomeCentre/amap-python#citing-amap).
        
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 :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6, <3.8
Description-Content-Type: text/markdown
Provides-Extra: dev
