Cellfinder version 1.3.0 is released!#

We are excited to announce that a new version of cellfinder has been released.

Main updates#

  • This update brings a significant change to the backend of cellfinder, as we have switched from TensorFlow to PyTorch. This change allows cellfinder to support python versions 3.11+, and simplifies the installation process. The new cellfinder version maintains the same classification accuracy. Models trained using previous versions of cellfinder will continue to work with the new version.

  • The default batch size used for detection has been increased to 64, which improves classification speed by approximately 40% on most systems. The batch size used for detection can now also be adjusted in the napari plugin.

What do I need to do?#

We recommend using a fresh conda environment to simplify the update. For GPU support, please follow the installation instructions in the documentation.

conda create -n cellfinder -c conda-forge python=3.11
conda activate cellfinder
pip install cellfinder

You can also update an existing installation of cellfinder using pip:

pip install --upgrade cellfinder

Classification performance#

The classification performance between the two versions is comparable. Below is a comparison of the performance between the two versions using data from the cellfinder paper. Running cellfinder with a PyTorch backend results in a comparable Pearson correlation and slightly improved linear best-fit slope (labelled as “coeff” in the plot) when comparing to manual cell counts. For more details on how the plots were generated, see the cellfinder paper.

TensorFlow backend#

PyTorch backend#