A review of microscopic cell imaging and neural network recognition for synergistic cyanobacteria identification and enumeration

Abstract

Real-time cyanobacteria/algal monitoring is a valuable tool for early detection of harmful algal blooms, water treatment efficacy evaluation, and assists tailored water quality risk assessments by considering taxonomy and cell counts. This review evaluates and proposes a synergistic approach using neural network image recognition and microscopic imaging devices by first evaluating published literature for both imaging microscopes and image recognition. Quantitative phase imaging was considered the most promising of the investigated imaging techniques due to the provision of enhanced information relative to alternatives. This information provides significant value to image recognition neural networks, such as the convolutional neural networks discussed within this review. Considering published literature, a cyanobacteria monitoring system and corresponding image processing workflow using in situ sample collection buoys and on-shore sample processing was proposed. This system can be implemented using commercially available equipment to facilitate accurate, real-time water quality monitoring.

Keywords: Cell recognition; Cyanobacteria; Cytometry; Imaging microscopy; Machine learning; Quantitative phase imaging; Workflow.

Fig. 1

Schematic diagram of QPI using Michelson interferometry. Figure [37] adapted from Min et al.

Fig. 2

Image post-processing workflow used by Min et al. [37]

Fig. 3

HoloConvNet performance under different testing arrangements. Here “DeepNN” refers to deep neural networks, “ConventionalNN” refers to conventional (single layer) neural networks, “Brightfield” refers to images collected using standard brightfield microscopy. Data [20] adapted from Jo et al.

Fig. 4

Cyanobacteria monitoring workflow from sample collection to image processing