Deep Learning Approach to Extract Geometric Features of Bacterial Cells in Biofilms

We develop a deep learning approach to estimate the geometric characteristics of bacterial biofilms grown on metal surfaces. Specifically, we focus on sulfate-reducing bacteria (SRB) which are widely implicated in microbiologically influenced corrosion (MIC) of metals, costing billions of dollars annually. Understanding the growth characteristics of biofilms is important for designing and developing protective coatings that effectively combat MIC. Our goal here is to automate the extraction of the shape and size characteristics of SRB bacterial cells from the scanning electron microscope (SEM) images of a biofilm generated at various growth stages. Typically, these geometric features are measured using laborious and manual methods. To automate this process, we use a combination of image processing and deep learning approaches to determine the geometric properties. This is done via image segmentation of SEM images using deep convolutional neural networks. To address the challenges associated with detection of individual cells that form clusters, we apply a modified watershed algorithm to separate the cells from the cluster. Finally, we estimate the number of cells as well as their length and width distributions.