Efficient cell detection in large images

    Workflow step:
    Image feature detection
    Image registration
    Image segmentation
    Image segmentationImage thresholding
    Image annotation
    Object classification
    Object feature extraction
    Image modality:
    Multi-photon microscopy
    Fluorescence microscopyLight-sheet microscopy

    License PyPI Python Version tests codecov Downloads Wheel Development Status Code style: black Imports: isort pre-commit Contributions Website Twitter

    Efficient cell detection in large images (e.g. whole mouse brain images)

    cellfinder-napari is a front-end to cellfinder-core to allow ease of use within the napari multidimensional image viewer. For more details on this approach, please see Tyson, Rousseau & Niedworok et al. (2021). This algorithm can also be used within the original cellfinder software for whole-brain microscopy analysis.

    cellfinder-napari, cellfinder and cellfinder-core were developed by Charly Rousseau and Adam Tyson in the Margrie Lab, based on previous work by Christian Niedworok, generously supported by the Sainsbury Wellcome Centre.


    Visualising detected cells in the cellfinder napari plugin



    Once you have installed napari. You can install napari either through the napari plugin installation tool, or directly from PyPI with:

    pip install cellfinder-napari


    Full documentation can be found here.

    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 or by raising an issue.



    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 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 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 Cassified cell candidates. Yellow - cells, Blue - artefacts


    Contributions to cellfinder-napari are more than welcome. Please see the developers guide.

    Citing cellfinder

    If you find this plugin useful, and use it in your research, please cite the paper 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

    If you use this, or any other tools in the brainglobe suite, please let us know, and we'd be happy to promote your paper/talk etc.


    • 0.1.0

    Last updated:

    • 21 August 2023

    First released:

    • 25 January 2021


    Supported data:

    Plugin type:

    GitHub activity:

    • Stars: 22
    • Forks: 6
    • Issues + PRs: 45

    Python versions supported:

    Operating system:


    • brainglobe-napari-io
    • cellfinder-core >=0.3
    • brainglobe-utils
    • magicgui
    • napari
    • napari-ndtiffs
    • napari-plugin-engine >=0.1.4
    • numpy
    • pooch >=1
    • qtpy
    • scikit-image
    • tifffile

    Sign up to receive updates