  The current study explores an artificial intelligence framework for measuring
the structural features from microscopy images of the bacterial biofilms.
Desulfovibrio alaskensis G20 (DA-G20) grown on mild steel surfaces is used as a
model for sulfate reducing bacteria that are implicated in microbiologically
influenced corrosion problems. Our goal is to automate the process of
extracting the geometrical properties of the DA-G20 cells from the scanning
electron microscopy (SEM) images, which is otherwise a laborious and costly
process. These geometric properties are a biofilm phenotype that allow us to
understand how the biofilm structurally adapts to the surface properties of the
underlying metals, which can lead to better corrosion prevention solutions. We
adapt two deep learning models: (a) a deep convolutional neural network (DCNN)
model to achieve semantic segmentation of the cells, (d) a mask
region-convolutional neural network (Mask R-CNN) model to achieve instance
segmentation of the cells. These models are then integrated with moment
invariants approach to measure the geometric characteristics of the segmented
cells. Our numerical studies confirm that the Mask-RCNN and DCNN methods are
227x and 70x faster respectively, compared to the traditional method of manual
identification and measurement of the cell geometric properties by the domain
experts.
