Deep learning to detect bacterial colonies for the production of vaccines

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 long, tedious, and subject to errors. In this work, multiple segmentation algorithms based on the U-Net CNN architecture are tested and proven to offer robust, automated CFU counting. It is also shown that the multiclass generalisation with a bespoke loss function allows virulent and avirulent colonies to be distinguished with acceptable accuracy. While many possibilities are left to explore, our results show the potential of deep learning for separating and classifying bacterial colonies. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

In this study, multiple segmentation algorithms based on the U-Net CNN architecture were used to develop a robust and automated CFU counting method. It was shown that the use of a bespoke loss function enabled accurate differentiation between virulent and avirulent colonies.