  During the development of vaccines, bacterial colony forming units (CFUs) are
counted in order to quantify the yield in the fermentation process. This manual
task is time-consuming and error-prone. In this work we test multiple
segmentation algorithms based on the U-Net CNN architecture and show that these
offer robust, automated CFU counting. We show that the multiclass
generalisation with a bespoke loss function allows distinguishing virulent and
avirulent colonies with acceptable accuracy. While many possibilities are left
to explore, our results show the potential of deep learning for separating and
classifying bacterial colonies.
