Artificial Intelligence-driven Image Analysis of Bacterial Cells and Biofilms

TL;DR

This paper presents an AI framework using deep learning models to automate the measurement of bacterial biofilm structures from microscopy images, significantly reducing analysis time and aiding corrosion research.

Contribution

It introduces a novel combination of deep learning segmentation models with geometric analysis to efficiently quantify biofilm cell structures from SEM images.

Findings

Mask R-CNN is 227x faster than manual methods.

DCNN achieves 70x speedup over traditional analysis.

Automated measurements can improve biofilm structural studies.

Abstract

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.