Use of CMEIAS Image Analysis Software to Accurately Compute Attributes of Cell Size, Morphology, Spatial Aggregation and Color Segmentation that Signify in Situ Ecophysiological Adaptations in Microbial Biofilm Communities

Tools of the Trade: Image Analysis Programs for Confocal Laser-Scanning Microscopy Studies of Biofilms and Considerations for Their Use by Experimental Researchers

Confocal laser-scanning microscopy (CLSM) is the bedrock of the microscopic visualization of biofilms. Previous applications of CLSM in biofilm studies have largely focused on observations of bacterial or fungal elements of biofilms, often seen as aggregates or mats of cells. However, the field of biofilm research is moving beyond qualitative observations alone, toward the quantitative analysis of the structural and functional features of biofilms, across clinical, environmental, and laboratory conditions. In recent times, several image analysis programs have been developed to extract and quantify biofilm properties from confocal micrographs. These tools not only vary in their scope and relevance to the specific biofilm features under study but also with respect to the user interface, compatibility with operating systems, and raw image requirements. Understanding these considerations is important when selecting tools for quantitative biofilm analysis, including at the initial experimental stages of image acquisition. In this review, we provide an overview of image analysis programs for confocal micrographs of biofilms, with a focus on tool selection and image acquisition parameters that are relevant for experimental researchers to ensure reliability and compatibility with downstream image processing.

Trends in forensic microbiology: From classical methods to deep learning

Forensic microbiology has been widely used in the diagnosis of causes and manner of death, identification of individuals, detection of crime locations, and estimation of postmortem interval. However, the traditional method, microbial culture, has low efficiency, high consumption, and a low degree of quantitative analysis. With the development of high-throughput sequencing technology, advanced bioinformatics, and fast-evolving artificial intelligence, numerous machine learning models, such as RF, SVM, ANN, DNN, regression, PLS, ANOSIM, and ANOVA, have been established with the advancement of the microbiome and metagenomic studies. Recently, deep learning models, including the convolutional neural network (CNN) model and CNN-derived models, improve the accuracy of forensic prognosis using object detection techniques in microorganism image analysis. This review summarizes the application and development of forensic microbiology, as well as the research progress of machine learning (ML) and deep learning (DL) based on microbial genome sequencing and microbial images, and provided a future outlook on forensic microbiology.

Micron-Scale Biogeography of Seawater Biofilm Colonies at Submersed Solid Substrata Affected by Organic Matter and Microbiome Transformation in the Baltic Sea

The aim of this research was to determine temporal and spatial evolution of biofilm architecture formed at model solid substrata submersed in Baltic sea coastal waters in relation to organic matter transformation along a one-year period. Several materials (metals, glass, plastics) were deployed for a certain time, and the collected biofilm-covered samples were studied with a confocal microscopy technique using the advanced programs of image analysis. The geometric and structural biofilm characteristics: biovolume, coverage fraction, mean thickness, spatial heterogeneity, roughness, aggregation coefficient, etc., turned out to evolve in relation to organic matter transformation trends, trophic water status, microbiome evolution, and biofilm micro-colony transition from the heterotrophic community (mostly bacteria) to autotrophic (diatom-dominated) systems. The biofilm morphology parameters allowed the substratum roughness, surface wettability, chromatic organisms colony adaptation to substrata, and quorum sensing or cell to cell signaling effects to be quantitatively evaluated. In addition to the previous work, the structural biofilm parameters could become further novel trophic state indicators.

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

With the acceleration of urbanization and living standards, microorganisms play an increasingly important role in industrial production, bio-technique, and food safety testing. Microorganism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The applications of the microorganism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image analysis methods with time. This study has high research significance and application value, which can be referred to as microbial researchers to comprehensively understand microorganism biovolume measurements using digital image analysis methods and potential applications.

A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches

Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

Quantitative image analysis of microbial communities with BiofilmQ

Biofilms are microbial communities that represent a highly abundant form of microbial life on Earth. Inside biofilms, phenotypic and genotypic variations occur in three-dimensional space and time; microscopy and quantitative image analysis are therefore crucial for elucidating their functions. Here, we present BiofilmQ—a comprehensive image cytometry software tool for the automated and high-throughput quantification, analysis and visualization of numerous biofilm-internal and whole-biofilm properties in three-dimensional space and time.

BiofilmQ is an image cytometry software tool that enables the visualization, quantification and analysis of biofilm properties, providing insights into their structure and function.

Alterations in the Endophyte-Enriched Root-Associated Microbiome of Rice Receiving Growth-Promoting Treatments of Urea Fertilizer and Rhizobium Biofertilizer

We examined the bacterial endophyte-enriched root-associated microbiome within rice (Oryza sativa) 55 days after growth in soil with and without urea fertilizer and/or biofertilization with a growth-promotive bacterial strain (Rhizobium leguminosarum bv. trifolii E11). After treatment to deplete rhizosphere/rhizoplane communities, washed roots were macerated and their endophyte-enriched communities were analyzed by 16S ribosomal DNA 454 amplicon pyrosequencing. This analysis clustered 99,990 valid sequence reads into 1105 operational taxonomic units (OTUs) with 97% sequence identity, 133 of which represented a consolidated core assemblage representing 12.04% of the fully detected OTU richness. Taxonomic affiliations indicated Proteobacteria as the most abundant phylum (especially α- and γ-Proteobacteria classes), followed by Firmicutes, Bacteroidetes, Verrucomicrobia, Actinobacteria, and several other phyla. Dominant genera included Rheinheimera, unclassified Rhodospirillaceae, Pseudomonas, Asticcacaulis, Sphingomonas, and Rhizobium. Several OTUs had close taxonomic affiliation to genera of diazotrophic rhizobacteria, including Rhizobium, unclassified Rhizobiales, Azospirillum, Azoarcus, unclassified Rhizobiaceae, Bradyrhizobium, Azonexus, Mesorhizobium, Devosia, Azovibrio, Azospira, Azomonas, and Azotobacter. The endophyte-enriched microbiome was restructured within roots receiving growth-promoting treatments. Compared to the untreated control, endophyte-enriched communities receiving urea and/or biofertilizer treatments were significantly reduced in OTU richness and relative read abundances. Several unique OTUs were enriched in each of the treatment communities. These alterations in structure of root-associated communities suggest dynamic interactions in the host plant microbiome, some of which may influence the well-documented positive synergistic impact of rhizobial biofertilizer inoculation plus low doses of urea-N fertilizer on growth promotion of rice, considered as one of the world's most important food crops.

Segmentation-driven Hierarchical RetinaNet for Detecting Protozoa in Micrograph

Protozoa detection and identification play important roles in many practical domains such as parasitology, scientific research, biological treatment processes, and environmental quality evaluation. Traditional laboratory methods for protozoan identification are time-consuming and require expert knowledge and expensive equipment. Another approach is using micrographs to identify the species of protozoans that can save a lot of time and reduce the cost. However, the existing methods in this approach only identify the species when the protozoan are already segmented. These methods study features of shapes and sizes. In this work, we detect and identify the images of cysts and oocysts of various species such as: Giardia lamblia, Iodamoeba butschilii, Toxoplasma gondi, Cyclospora cayetanensis, Balantidium coli, Sarcocystis, Cystoisospora belli and Acanthamoeba, which have round shapes in common and affect human and animal health seriously. We propose Segmentation-driven Hierarchical RetinaNet to automatically detect, segment, and identify protozoans in their micrographs. By applying multiple techniques such as transfer learning, and data augmentation techniques, and dividing training samples into life-cycle stages of protozoans, we successfully overcome the lack of data issue in applying deep learning for this problem. Even though there are at most 5 samples per life-cycle category in the training data, our proposed method still achieves promising results and outperforms the original RetinaNet on our protozoa dataset.