cellfinder 1.3.2

Automated 3D cell detection in large microscopy images

cellfinder

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). There are three different ways to interact and use it, each with different user interfaces and objectives in mind. For more details, head over to the documentation website.

At a glance:

• There is a command-line interface called brainmapper that integrates with brainreg for automated cell detection and classification. You can install it through brainglobe-workflows.
• There is a napari plugin for interacting graphically with the cellfinder tool.
• There is a Python API to allow users to integrate BrainGlobe tools into their custom workflows.

Installation

You can find the installation instructions on the BrainGlobe website, which will go into more detail about the installation process if you want to minimise your installation to suit your needs. However, we recommend that users install cellfinder either through installing BrainGlobe version 1, or (if you also want the command-line interface) installing brainglobe-workflows.

# If you want to install all BrainGlobe tools, including cellfinder, in a consistent manner with one command:
pip install brainglobe>=1.0.0
# If you want to install the brainmapper CLI tool as well:
pip install brainglobe-workflows>=1.0.0

If you only want the cellfinder package by itself, you can pip install it alone:

pip install cellfinder>=1.0.0

Be sure to specify a version greater than version v1.0.0 - prior to this version the cellfinder package had a very different structure that is incompatible with BrainGlobe version 1 and the other tools in the BrainGlobe suite. See our blog posts for more information on the release of BrainGlobe version 1.

Seeking help or contributing

We are always happy to help users of our tools, and welcome any contributions. If you would like to get in contact with us for any reason, please see the contact page of our website.

Citation

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

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.