Binarization is a key process in microscopy cell counting and cytometry analysis that is performed before segmentation to identify a cell within the background. We test the performances of 16 global and 9 local ImageJ thresholding algorithms on both experimental and synthetic confocal images of Escherichia coli and Staphylococcus aureus, evaluating the misclassification errors according to standard pattern recognition parameters. Some thresholding algorithms, such as Otsu, outperform other approaches, with respect to a pixel-by-pixel analysis. Overall, we found that the Bernsen local thresholding furnishes the best results also with respect to cell counting and morphology analysis.
Background
In recent years, a variety of imaging techniques operating at nanoscale resolution have been reported. These techniques have the potential to enrich our understanding of bacterial species relevant to human health, such as antibiotic-resistant pathogens. However, owing to the novelty of these techniques, their use is still confined to addressing very particular applications, and their availability is limited owing to associated costs and required expertise. Among these, scattering-type scanning near field optical microscopy (s-SNOM) has been demonstrated as a powerful tool for exploring important optical properties at nanoscale resolution, depending only on the size of a sharp tip. Despite its huge potential to resolve aspects that cannot be tackled otherwise, the penetration of s-SNOM into the life sciences is still proceeding at a slow pace for the aforementioned reasons.
Results
In this work we introduce SSNOMBACTER, a set of s-SNOM images collected on 15 bacterial species. These come accompanied by registered Atomic Force Microscopy images, which are useful for placing nanoscale optical information in a relevant topographic context.
Conclusions
The proposed dataset aims to augment the popularity of s-SNOM and for accelerating its penetration in life sciences. Furthermore, we consider this dataset to be useful for the development and benchmarking of image analysis tools dedicated to s-SNOM imaging, which are scarce, despite the high need. In this latter context we discuss a series of image processing and analysis applications where SSNOMBACTER could be of help.
Super‐resolution microscopy techniques can provide answers to still pending questions on prokaryotic organisms but are yet to be used at their full potential for this purpose. To address this, we evaluate the ability of the rhodamine‐like KK114 dye to label various types of bacteria, to enable imaging of fine structural details with stimulated emission depletion microscopy (STED). We assessed fluorescent labeling with KK114 for eleven Gram‐positive and Gram‐negative bacterial species and observed that this contrast agent binds to their cell membranes. Significant differences in the labeling outputs were noticed across the tested bacterial species, but importantly, KK114‐staining allowed the observation of subtle nanometric cell details in some cases. For example, a helix pattern resembling a cytoskeleton arrangement was detected in Bacillus subtilis. Furthermore, we found that KK114 easily penetrates the membrane of bacterial microorganism that lost their viability, which can be useful to discriminate between living and dead cells.
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