cellfinder is software for automated 3D cell detection in very large 3D images (e.g., serial two-photon or lightsheet volumes of whole mouse brains).

Detected labelled cells, overlaid on a segmented coronal brain section Detected labelled cells, overlaid on a segmented coronal brain section

Ways to use cellfinder#

cellfinder exists as three separate software packages with different user interfaces and different aims.


If you don’t know how to start, we recommend the cellfinder napari plugin.


cellfinder-core is a Python package implementing the core algorithm for efficient cell detection in large images.

The package exists on its own to allow developers to implement the algorithm in their own software. For now, the only API documentation is in the GitHub README, please see the documentation for the napari plugin here for an explanation of the parameters. Alternatively, please get in touch.

cellfinder napari plugin#

This is a thin wrapper around the cellfinder-core package and aims to:

  • Provide the cell detection algorithm in a user-friendly form

  • Allow the cell detection algorithm to be chained together with other tools in the Napari ecosystem

  • Allow easier parameter optimisation for users of the other cellfinder tools.

Visualising detected cells in the cellfinder napari plugin Visualising detected cells in the cellfinder napari plugin

cellfinder command-line tool#

A command-line tool (confusingly just called cellfinder) exists to combine the cellfinder-core cell detection algorithm and brainreg. cellfinder can:

  • Detect labelled cells in 3D in whole-brain images (many hundreds of GB)

  • Register the image to an atlas (such as the Allen Mouse Brain Atlas)

  • Segment the brain based on the reference atlas

  • Calculate the volume of each brain area, and the number of labelled cells within it

  • Transform everything into standard space for analysis and visualisation


User guide#


Citing cellfinder#

If you find cellfinder 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 any of the image registration functions in cellfinder, please also cite brainreg.