Skip to main content

Efficient cell detection in large images

Project description


License PyPI Python Version tests codecov Downloads Wheel Development Status Code style: black Contributions Website Twitter

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) for napari, based on the cellfinder-core package.

This algorithm can also be used within the original cellfinder software for whole-brain microscopy analysis.


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 by email, 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

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’ PLOS Computational Biology, 17(5), e1009074

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.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for cellfinder-napari, version 0.0.18
Filename, size File type Python version Upload date Hashes
Filename, size cellfinder_napari-0.0.18-py3-none-any.whl (36.9 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size cellfinder-napari-0.0.18.tar.gz (42.4 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page