  We present an application of the well-known Mask R-CNN approach to the
counting of different types of bacterial colony forming units that were
cultured in Petri dishes. Our model was made available to lab technicians in a
modern SPA (Single-Page Application). Users can upload images of dishes, after
which the Mask R-CNN model that was trained and tuned specifically for this
task detects the number of BVG- and BVG+ colonies and displays these in an
interactive interface for the user to verify. Users can then check the model's
predictions, correct them if deemed necessary, and finally validate them. Our
adapted Mask R-CNN model achieves a mean average precision (mAP) of 94\% at an
intersection-over-union (IoU) threshold of 50\%. With these encouraging
results, we see opportunities to bring the benefits of improved accuracy and
time saved to related problems, such as generalising to other bacteria types
and viral foci counting.
