BrainGlobe version 1 is here! Head over to the blog to find out more


Improving algorithm performance#

brainmapper detects cells in a two-stage process, firstly cell candidates are detected. These are cell-like objects of approximately the correct intensity and size. These cell candidates are then classified into cells and artefacts by a deep learning step.

Cell candidate detection#

If the initial cell candidate detection is not performing well, then we suggest adjusting the cell detection parameters. For a better understanding of what these parameters do, it may be useful to consult the original PLOS Computational Biology paper.


In general, false positives (non-cells being detected) is generally ok, as these will be refined in the classification step.

Cell candidate classification#

The classification will use a pre-trained network by default that is included with the software. This network will usually need to be retrained. For more details, please see the guide to retraining the pre-trained network.

Fixing technical problems#

As brainmapper relies on a number of third party libraries, notably

there may be issues while running the software. If you are having any issues, please see the following sections:

Anything else#

If you are still having trouble, please get in touch.