A Deep Learning Approach to Bacterial Colony Segmentation

In this paper, we introduce a new method for the segmentation of bacterial colonies in solid agar plate images. The proposed approach comprises two contributions. First, a simple but nonetheless effective engine is devised to generate synthetic plate images. This engine overlays bacterial colony patches to existing background images, taking into account both the local appearance of the background and the intrinsic opacity of the bacterial colonies. Therefore, a scalable alternative to the human ground-truth supervision-often difficult to obtain in medical imaging, due to privacy issues and scarcity of data-is provided. Then, synthetic generated data, together with few annotated images, were used to train a Fully-Convolutional Network. Such network is actually effective in separating bacterial colonies from the background. Finally, we discuss the role of the generation of synthetic images, conducting experiments that show how their inclusion improves the performances of the segmentation network, producing very encouraging results.