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.
@article{arxiv.2009.00926,
title = {Deep Learning to Detect Bacterial Colonies for the Production of Vaccines},
author = {Thomas Beznik and Paul Smyth and Gaël de Lannoy and John A. Lee},
journal= {arXiv preprint arXiv:2009.00926},
year = {2020}
}
Comments
6 pages, 2 figures, accepted at ESANN 2020 (European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning)