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

Abstract

Abstract:In this review, we describe computational features of computer-assisted microscopy that are unique to the Center for Microbial Ecology Image Analysis System (CMEIAS) software, and examples illustrating how they can be used to gain ecophysiological insights into microbial adaptations occurring at micrometer spatial scales directly relevant to individual cells occupying their ecological niches in situ. These features include algorithms that accurately measure (1) microbial cell length relevant to avoidance of protozoan bacteriovory; (2) microbial biovolume body mass relevant to allometric scaling and local apportionment of growth-supporting nutrient resources; (3) pattern recognition rules for morphotype classification of diverse microbial communities relevant to their enhanced fitness for success in the particular habitat; (4) spatial patterns of coaggregation that reveal the local intensity of cooperative vs. competitive adaptations in colonization behavior relevant to microbial biofilm ecology; and (5) object segmentation of complex color images to differentiate target microbes reporting successful cell-cell communication. These unique computational features contribute to the CMEIAS mission of developing accurate and freely accessible tools of image bioinformatics that strengthen microscopy-based approaches for understanding microbial ecology at single-cell resolution. OPEN ACCESSComputation 2015, 3 73

Influence of Substratum Hydrophobicity on the Geomicrobiology of River Biofilm Architecture and Ecology Analyzed by CMEIAS Bioimage Informatics

“…The mission of our CMEIAS project is to develop, document and release a comprehensive suite of bioimage informatics analysis software applications designed to strengthen quantitative, microscopy-based approaches for understanding microbial ecology, at spatial scales directly relevant to microbes and their ecological niches without the need for cultivation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The wealth of information gained by CMEIAS analysis of digital images can bridge with other modern genotypic and phenotypic technologies to fill knowledge gaps revealing additional insights of in situ phenotypic characteristics of ecological importance to microbial cells, populations, communities and microbiomes.…”

“…The wealth of information gained by CMEIAS analysis of digital images can bridge with other modern genotypic and phenotypic technologies to fill knowledge gaps revealing additional insights of in situ phenotypic characteristics of ecological importance to microbial cells, populations, communities and microbiomes. Examples include their biodiversity, productivity (conversion of available nutrient resources into biomass and metabolic energy), food-web dynamics, landscape ecology, strategies of successful colonization behavior, adaptations and resilience to environmental stresses, and intensities of interaction with each other within biofilms [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. When finalized, the copyrighted CMEIAS software tools and their comprehensive documentations are released as free downloads at our project website [1].…”

Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

“…Individualized numerical simulations of physiological processes in the human body received a great deal of attention over several decades, and a vast number of models have been described in the literature. Contemporary resolution of medical images and new algorithms for their post-processing allow us to develop high resolution numerical models of various processes at cellular-, organ-, and whole organism-levels [1][2][3][4][5]. Given an imaging dataset, one performs image segmentation, volume reconstruction, and numerical discretization.…”

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

“…3 a composite images of microbial communities in this paper is shown, showing the microorganism classification work on the 'microorganism population' level. As a further development, the AM classification system is continuously improved to enhance its functions and stability in Dazzo and Gross (2013a, b), Dazzo and Niccum (2015), Ji et al (2015).…”

“…Crossed work Besides the related work above, AM classification approaches using CBMIA methods (Dazzo and Gross 2013a, b;Dazzo and Niccum 2015;Ji et al 2015) mentioned in Sect. 2 are also involved in the EM CBMIA classification domain.…”