Automated quality assessment of autonomously acquired microscopic images of fluorescently stained bacteria

Zeder, Michael; Kohler, Esther

doi:10.1002/cyto.a.20810

Cited by 25 publications

(26 citation statements)

References 26 publications

(18 reference statements)

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“…S1G in the supplemental material). For each channel, the exposure time (constant or variable), focusing procedure, and number of images per z-stack for the compensation of filter unevenness were selected (20). Notably, for each channel, only one exposure time, either constant or variable, could be selected.…”

Section: Methodsmentioning

confidence: 99%

“…S1H in the supplemental material), and low-quality images, such as images with over-or underexposed parts, areas out of focus (unevenness), or too many aggregates, large phytoplankton cells, debris, and particles, were excluded from further analysis to ensure high data quality (see Fig. S3 to S6 in the supplemental material) (20). In a second step, a so-called metafile was calculated from the remaining HQ images for faster image processing (see Fig.…”

Section: Methodsmentioning

confidence: 99%

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Modification of a High-Throughput Automatic Microbial Cell Enumeration System for Shipboard Analyses

Bennke

Reintjes

Schattenhofer

et al. 2016

Appl Environ Microbiol

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In the age of ever-increasing "-omics" studies, the accurate and statistically robust determination of microbial cell numbers within often-complex samples remains a key task in microbial ecology. Microscopic quantification is still the only method to enumerate specific subgroups of microbial clades within complex communities by, for example, fluorescence in situ hybridization (FISH). In this study, we improved an existing automatic image acquisition and cell enumeration system and adapted it for usage at high seas on board an oceanographic research ship. The system was evaluated by testing settings such as minimal pixel area and image exposure times ashore under stable laboratory conditions before being brought on board and tested under various wind and wave conditions. The system was robust enough to produce high-quality images even with ship heaves of up to 3 m and pitch and roll angles of up to 6.3°. On board the research ship, on average, 25% of the images acquired from plankton samples on filter membranes could be used for cell enumeration. Automated enumeration was highly correlated with manual counts (r 2 > 0.9). Even the smallest of microbial cells in the open ocean, members of the alphaproteobacterial SAR11 clade, could be confidently detected and enumerated. The automated image acquisition and cell enumeration system developed here enables an accurate and reproducible determination of microbial cell counts in planktonic samples and allows insight into the abundance and distribution of specific microorganisms already on board within a few hours. IMPORTANCEIn this research article, we report on a new system and software pipeline, which allows for an easy and quick image acquisition and the subsequent enumeration of cells in the acquired images. We put this pipeline through vigorous testing and compared it to manual microscopy counts of microbial cells on membrane filters. Furthermore, we tested this system at sea on board a marine research vessel and counted bacteria on board within a few hours after the retrieval of water samples. The imaging and counting system described here has been successfully applied to a number of laboratory-based studies and allowed the quantifi- T he exact quantification of cells is fundamental to microbial ecology and hence for understanding the interaction of microorganisms with biotic and abiotic factors. Still, the counting of microbial cells on membrane filters using an epifluorescence microscope remains the method of choice (1-3) for the enumeration of picoplankton cells, although it is rather time-consuming and relies on the experience of the individual person counting. Additionally, manual counting of an entire membrane filter is not practical within a given time frame, and therefore, only a small part is analyzed (usually 12 to 20 fields of view [FOVs]) (3, 4). Consequently, several tools have been developed over the past 2 decades to automatically enumerate microbial cells by means of image acquisition and subsequent image analysis (e.g., references 5-13). Mos...

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Modification of a High-Throughput Automatic Microbial Cell Enumeration System for Shipboard Analyses

Bennke

Reintjes

Schattenhofer

et al. 2016

Appl Environ Microbiol

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“…In Zeder et al (2010), a unary-class classification approach is introduced to distinguish fluorescently stained bacteria, where spatially invariant estimators are used as features to represent the SMs first, and then an ANN classifier is designed. In the experiment, 25,000 microscopic images are applied to evaluate the classification performance, and finally a classification accuracy of 94% is obtained.…”

Section: Overview Of Sm Classificationmentioning

confidence: 99%

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

Wang

2017

Artif Intell Rev

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Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semior full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future devel-

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“…An experimentalist should therefore keep environmental conditions as constant as possible. Data quality and reproducibility can be assessed by automated quality control (Zeder et al, 2010) and by incorporating control treatments in the assay. Differences in image features resulting from experimental variations are unlikely to be become obvious in the evaluation of the machine-learning method itself and thus have to be avoided early on in data acquisition and sample preparation.…”

Section: Some Experimental Design Guidelinesmentioning

confidence: 99%

Machine learning in cell biology – teaching computers to recognize phenotypes

Sommer

Gerlich

2013

Journal of Cell Science

276

237

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SummaryRecent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.

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Automated quality assessment of autonomously acquired microscopic images of fluorescently stained bacteria

Cited by 25 publications

References 26 publications

Modification of a High-Throughput Automatic Microbial Cell Enumeration System for Shipboard Analyses

Modification of a High-Throughput Automatic Microbial Cell Enumeration System for Shipboard Analyses

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

Machine learning in cell biology – teaching computers to recognize phenotypes

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