Automated counting of bacterial colonies by image analysis

An experimental setup and segmentation method for CFU counting on agar plate for the assessment of drinking water

Quantification of bacterial colonies on an agar plate is a daily routine for a microbiologist to determine the number of viable microorganisms in the sample. In general, microbiologists perform a visual assessment of bacterial colonies which is time-consuming (takes 2 min per plate), tedious, and subjective. Some automatic counting algorithms are developed that save labour and time, but their results are affected by the non-illumination on an agar plate. To improve this, the present manuscript aims to develop an inexpensive and efficient device to acquire S.aureus images via an automatic counting method using image processing techniques under real laboratory conditions. The proposed method (P_ColonyCount) includes the region of interest extraction and color space transformation followed by filtering, thresholding, morphological operation, distance transform, and watershed technique for the quantification of isolated and overlapping colonies. The present work also shows a comparative study on grayscale, K, and green channels by applying different filter and thresholding techniques on 42 images. The results of all channels were compared with the score provided by the expert (manual count). Out of all the proposed method (P_ColonyCount), the K channel gives the best outcome in comparison with the other two channels (grayscale and green) in terms of precision, recall, and F-measure which are 0.99, 0.99, and 0.99 (2 h), 0.98, 0.99, and 0.98 (4 h), and 0.98, 0.98, 0.98 (6 h) respectively. The execution time of the manual and the proposed method (P_ColonyCount) for 42 images ranges from 19 to 113 s and 15 to 31 s respectively. Apart from this, a user-friendly graphical user interface is also developed for the convenient enumeration of colonies without any expert knowledge/training. The developed imaging device will be useful for researchers and teaching lab settings.

Evaluation of an Automated System for the Counting of Microbial Colonies

Counting of microbial colonies is a common technique employed in research and diagnostics. To simplify this tedious and time-consuming process, automated systems have been proposed. This study aimed to elucidate the reliability of automated colony counting. We evaluated a commercially available instrument (UVP ColonyDoc-It Imaging Station) in regard to its accuracy and potential time savings. Suspensions of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Enterococcus faecium, and Candida albicans (n = 20 each) were adjusted to achieve growth of approximately 1,000, 100, 10, and 1 colony per plate, respectively, after overnight incubation on different solid media. Compared with manual counting, each plate was automatically counted by the UVP ColonyDoc-It with and without visual adjustment on a computer display. For all bacterial species and concentrations automatically counted without visual correction, an overall mean difference from manual counts of 59.7%, a proportion of isolates with overestimation/underestimation of colony numbers of 29%/45%, respectively, and only a moderate relationship (R2 = 0.77) with the manual counting were shown. Applying visual correction, the overall mean difference from manual counts was 1.8%, the proportion of isolates with overestimation/underestimation of colony numbers amounted to 2%/42%, respectively, and a strong relationship (R2 = 0.99) with the manual counting was observed. The mean time needed for manual counting compared with automated counting without and with visual correction was 70 s, 30 s, and 104 s, respectively, for bacterial colonies through all concentrations tested. Generally, similar performance regarding accuracy and counting time was observed with C. albicans. In conclusion, fully automatic counting showed low accuracy, especially for plates with very high or very low colony numbers. After visual correction of the automatically generated results, the concordance with manual counts was high; however, there was no advantage in reading time. IMPORTANCE Colony counting is a widely utilized technique in the field of microbiology. The accuracy and convenience of automated colony counters are essential for research and diagnostics. However, there is only sparse evidence on performance and usefulness of such instruments. This study examined the current state of reliability and practicality of the automated colony counting with an advanced modern system. For this, we thoroughly evaluated a commercially available instrument in terms of its accuracy and counting time required. Our findings indicate that fully automatic counting resulted in low accuracy, particularly for plates with very high or very low colony numbers. Visual correction of the automated results on a computer screen improved concordance with manual counts, but there was no benefit in counting time.

Cryostructuring of Polymeric Systems: 64. Preparation and Properties of Poly(vinyl alcohol)-Based Cryogels Loaded with Antimicrobial Drugs and Assessment of the Potential of Such Gel Materials to Perform as Gel Implants for the Treatment of Infected Wounds

Physical macroporous poly(vinyl alcohol)-based cryogels formed by the freeze-thaw technique without the use of any foreign cross-linkers are of significant interests for biomedical applications. In the present study, such gel materials loaded with the antimicrobial substances were prepared and their physicochemical properties were evaluated followed by an assessment of their potential to serve as drug carriers that can be used as implants for the treatment of infected wounds. The antibiotic Ceftriaxone and the antimycotic Fluconazole were used as antimicrobial agents. It was shown that the Ceftriaxone additives caused the up-swelling effects with respect to the cryogel matrix and some decrease in its heat endurance but did not result in a substantial change in the gel strength. With that, the drug release from the cryogel vehicle occurred without any diffusion restrictions, which was demonstrated by both the spectrophotometric recording and the microbiological agar diffusion technique. In turn, the in vivo biotesting of such drug-loaded cryogels also showed that these materials were able to function as rather efficient antimicrobial implants injected in the artificially infected model wounds of laboratory rabbits. These results confirmed the promising biomedical potential of similar implants.

Image Analysis Semi-Automatic System for Colony-Forming-Unit Counting

Background: Accurate quantitative analysis of microorganisms is recognized as an essential tool for gauging safety and quality in microbiology settings in a wide range of fields. The enumeration process of viable microorganisms via traditional culturing techniques are methodically convenient and cost-effective, conferring high applicability worldwide. However, manual counting can be time-consuming, laborious and imprecise. Furthermore, particular cases require an urgent and accurate response for effective processing. Methods: To reduce time limitations and discrepancies, this work introduces an image processing method capable of semi-automatically quantifying the number of colony forming units (CFUs). This rapid enumeration technique enables the technician to provide an expeditious assessment of the microbial load of a given sample. To test and validate the system, three bacterial species were cultured, and a labeled database was created, with subsequent image acquisition. Results: The system demonstrated acceptable classification measures; the mean values of Accuracy, Recall and F-measure were: (1) 95%, 95% and 0.95 for E. coli; (2) 91%, 91% and 0.90 for P. aeruginosa; and (3) 84%, 86% and 0.85 for S. aureus. Conclusions: Evidence related to the time-saving potential of the system was achieved; the time spent on quantification tasks of plates with a high number of colonies might be reduced to a half and occasionally to a third.

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.

Tuning of Electrode Surface for Enhanced Bacterial Adhesion and Reactions: A Review on Recent Approaches

The study of bacterial adhesion and its consequences has great significance in different fields such as marine science, renewable energy sectors, soil and plant ecology, food industry, and the biomedical field. Generally, the adverse effects of microbial surface interactions have attained wide visibility. However, herein, we present distinct approaches to highlight the beneficial aspects of microbial surface interactions for various applications rather than deal with the conventional negative aspects or prevention strategies. The surface microbial reactions can be tuned for useful biochemical or bio-electrochemical applications, which are otherwise unattainable through conventional routes. In this context, the present review is a comprehensive approach to highlight the basic principles and signature parameters that are responsible for the useful microbial-electrode interactions. It also proposes various surface tuning strategies, which are useful for tuning the electrode characteristics particularly suitable for the enhanced bacterial adhesion and reactions. The tuning of surface characteristics of electrodes is discussed with a special reference to the Microbial Fuel Cell as an example.

A quantum-inspired classifier for clonogenic assay evaluations

Recent advances in Quantum Machine Learning (QML) have provided benefits to several computational processes, drastically reducing the time complexity. Another approach of combining quantum information theory with machine learning—without involving quantum computers—is known as Quantum-inspired Machine Learning (QiML), which exploits the expressive power of the quantum language to increase the accuracy of the process (rather than reducing the time complexity). In this work, we propose a large-scale experiment based on the application of a binary classifier inspired by quantum information theory to the biomedical imaging context in clonogenic assay evaluation to identify the most discriminative feature, allowing us to enhance cell colony segmentation. This innovative approach offers a two-fold result: (1) among the extracted and analyzed image features, homogeneity is shown to be a relevant feature in detecting challenging cell colonies; and (2) the proposed quantum-inspired classifier is a novel and outstanding methodology, compared to conventional machine learning classifiers, for the evaluation of clonogenic assays.

Efficient microbial colony growth dynamics quantification with ColTapp, an automated image analysis application

Populations of genetically identical bacteria are phenotypically heterogeneous, giving rise to population functionalities that would not be possible in homogeneous populations. For instance, a proportion of non-dividing bacteria could persist through antibiotic challenges and secure population survival. This heterogeneity can be studied in complex environmental or clinical samples by spreading the bacteria on agar plates and monitoring time to growth resumption in order to infer their metabolic state distribution. We present ColTapp, the Colony Time-lapse application for bacterial colony growth quantification. Its intuitive graphical user interface allows users to analyze time-lapse images of agar plates to monitor size, color and morphology of colonies. Additionally, images at isolated timepoints can be used to estimate lag time. Using ColTapp, we analyze a dataset of Staphylococcus aureus time-lapse images including populations with heterogeneous lag time. Colonies on dense plates reach saturation early, leading to overestimation of lag time from isolated images. We show that this bias can be corrected by taking into account the area available to each colony on the plate. We envision that in clinical settings, improved analysis of colony growth dynamics may help treatment decisions oriented towards personalized antibiotic therapies.

Use of digital images to count colonies of biodiesel deteriogenic microorganisms

This work presents a novel, robust procedure for the semi-automated counting of colony-forming units of Bacillus pumilus (a bacterium) and Meyerozyma guilliermondii (a yeast) both isolated from diesel oil. The counting is performed from digital images of Petri dishes containing the samples by a developed Python code, and the images are acquired from a low-cost scanning apparatus. The counting algorithm is based on the similar morphological characteristics of the bacterium and the yeast colonies. It was compared with classical counting methodology, and the results showed calibration and validation curves with a coefficient of determination (R2) of 0.99 and 0.98, respectively. The developed methodology is a valuable alternative to estimate the microbial contamination of biofuels.

Evaluation of gas holes in "Queijo de Nisa" PDO cheese using computer vision

"Queijo de Nisa" is a traditional Portuguese cheese, granted with PDO label, produced with raw ewe's milk in which the aqueous extract of cardoon flower Cynara cardunculus L. is the only coagulant allowed. As in similar cheeses with no use of starter cultures or pasteurisation, the quality and food safety are depending on prevention, high hygienic standards and a proper manufacturing process. This study investigated the use of computer vision as novel method for the evaluation of gas holes in Queijo de Nisa in three different ripening dates (0, 15 and 35 days). A total of 48 samples were produced using cardoon flower from three different origins (C1, C2 and C3) and a commercial vegetable coagulant (C4). The results presented a high correlation between image-dependent attributes and physical-chemical properties during ripening time, especially within the first 15 days of ripening time, where major structural changes were observed inside the Queijo de Nisa cheese. Principal component analysis presented a strong correlation (p < 0.05) between image parameters and the physical-chemical evolution until 15 days. From 15 to 35 days, the evolution of cheeses was mainly depending on structural parameters, like G'_1 Hz and hardness. No influence was observed due to the geographical origin of cardoon flower.

Experimental setup and image processing method for automatic enumeration of bacterial colonies on agar plates

Automated colony counting methods have long been known in Microbiology. Numerous methods for automated image analysis have been described and a wide range of commercial products exists. Known advantages are saving cost by reducing enumeration time, automatic documentation, reproducibility, and operator independence. Still, even today the realization of all advantages of automated image analysis makes it necessary to either invest in an expensive, high performance commercial system, or to acquire expert knowledge in image processing. This is a considerable obstacle for many laboratories, and the reason why manual colony counting is still done frequently. This article describes an easy to apply automatic colony counting system–including suggestions for sample preparation–that can be put into operation with basic knowledge of image processing and low budget.

MF2C3: Multi-Feature Fuzzy Clustering to Enhance Cell Colony Detection in Automated Clonogenic Assay Evaluation

A clonogenic assay is a biological technique for calculating the Surviving Fraction (SF) that quantifies the anti-proliferative effect of treatments on cell cultures: this evaluation is often performed via manual counting of cell colony-forming units. Unfortunately, this procedure is error-prone and strongly affected by operator dependence. Besides, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or administered cytotoxic agent. Relying upon the direct proportional relationship between the Area Covered by Colony (ACC) and the colony count and size, along with the growth rate, we propose MF2C3, a novel computational method leveraging spatial Fuzzy C-Means clustering on multiple local features (i.e., entropy and standard deviation extracted from the input color images acquired by a general-purpose flat-bed scanner) for ACC-based SF quantification, by considering only the covering percentage. To evaluate the accuracy of the proposed fully automatic approach, we compared the SFs obtained by MF2C3 against the conventional counting procedure on four different cell lines. The achieved results revealed a high correlation with the ground-truth measurements based on colony counting, by outperforming our previously validated method using local thresholding on L*u*v* color well images. In conclusion, the proposed multi-feature approach, which inherently leverages the concept of symmetry in the pixel local distributions, might be reliably used in biological studies.

TAMMiCol: Tool for analysis of the morphology of microbial colonies

Many microbes are studied by examining colony morphology via two-dimensional top-down images. The quantification of such images typically requires each pixel to be labelled as belonging to either the colony or background, producing a binary image. While this may be achieved manually for a single colony, this process is infeasible for large datasets containing thousands of images. The software Tool for Analysis of the Morphology of Microbial Colonies (TAMMiCol) has been developed to efficiently and automatically convert colony images to binary. TAMMiCol exploits the structure of the images to choose a thresholding tolerance and produce a binary image of the colony. The images produced are shown to compare favourably with images processed manually, while TAMMiCol is shown to outperform standard segmentation methods. Multiple images may be imported together for batch processing, while the binary data may be exported as a CSV or MATLAB MAT file for quantification, or analysed using statistics built into the software. Using the in-built statistics, it is found that images produced by TAMMiCol yield values close to those computed from binary images processed manually. Analysis of a new large dataset using TAMMiCol shows that colonies of Saccharomyces cerevisiae reach a maximum level of filamentous growth once the concentration of ammonium sulfate is reduced to 200 μM. TAMMiCol is accessed through a graphical user interface, making it easy to use for those without specialist knowledge of image processing, statistical methods or coding.

Many microbes are studied by examining the colony morphology via a two-dimensional top-down image. In order to quantify such images, we typically need to label each pixel as belonging either to the colony or the background, creating a binary image. This task is laborious when performed manually and proves infeasible for large datasets. To overcome this, we have developed the software Tool for Analysis of the Morphology of Microbial Colonies (TAMMiCol), which automatically and efficiently converts colony images to binary. Multiple images may be imported and processed simultaneously, and TAMMiCol exploits the structure of the images to identify an appropriate threshold for the binary conversion of each image. The images produced by TAMMiCol, which take around 20 seconds each to process, compare favourably with images processed manually, which take anywhere up to 15 minutes, while TAMMiCol outperforms several standard image segmentation methods. After processing, the images may be exported as a CSV or MATLAB MAT file for further analysis, or may be quantified by TAMMiCol using the in-built statistics. Using TAMMiCol, we have found that colonies of S. cerevisiae reach a maximum level of filamentous growth once the concentration of ammonium sulfate is reduced to 200 μM.

Automated counting of bacterial colonies on agar plates based on images captured at near-infrared light

Counting colonies is usually used in microbiological analysis to assess if samples meet microbiological criteria. Although manual counting remains gold standard, the process is subjective, tedious, and time-consuming. Some developed automatic counting methods could save labors and time, but their results are easily affected by uneven illumination and reflection of visible light. To offer a method which counts colonies automatically and is robust to light, we constructed a convenient and cost-effective system to obtain images of colonies at near-infrared light, and proposed an automatic method to detect and count colonies by processing images. The colonies cultured by using raw cows' milk were used as identification objects. The developed system mainly consisted of a visible/near-infrared camera and a circular near-infrared illuminator. The automatic method proposed to count colonies includes four steps, i.e., eliminating noises outside agar plate, removing plate rim and wall, identifying and separating clustered or overlapped colonies, and counting colonies by using connected region labelling, distance transform, and watershed algorithms, etc. A user-friendly graphic user interface was also developed for the proposed method. The relative error and counting time of the automatic counting method were compared with those of manual counting. The results showed that the relative error of the automatic counting method was -7.4%~ + 8.3%, with average relative error of 0.2%, and the time used for counting colonies on each agar plate was 11-21 s, which was 15-75% of the time used in manual counting, depending on the numbers of colonies on agar plates. The proposed system and automatic counting method demonstrate promising performance in terms of precision, and they are robust and efficient in terms of labor- and time-savings.

Simple and Precise Counting of Viable Bacteria by Resazurin-Amplified Picoarray Detection

Simple, fast, and precise counting of viable bacteria is fundamental to a variety of microbiological applications such as food quality monitoring and clinical diagnosis. To this end, agar plating, microscopy, and emerging microfluidic devices for single bacteria detection have provided useful means for counting viable bacteria, but they also have their limitations ranging from complexity, time, and inaccuracy. We present herein our new method RAPiD (Resazurin-Amplified Picoarray Detection) for addressing this important problem. In RAPiD, we employ vacuum-assisted sample loading and oil-driven sample digitization to stochastically confine single bacteria in Picoarray, a microfluidic device with picoliter-sized isolation chambers (picochambers), in <30 s with only a few minutes of hands-on time. We add AlamarBlue, a resazurin-based fluorescent dye for bacterial growth, in our assay to accelerate the detection of "microcolonies" proliferated from single bacteria within picochambers. Detecting fluorescence in picochambers as an amplified surrogate for bacterial cells allows us to count hundreds of microcolonies with a single image taken via wide-field fluorescence microscopy. We have also expanded our method to practically test multiple titrations from a single bacterial sample in parallel. Using this expanded "multi-RAPiD" strategy, we can quantify viable cells in E. coli and S. aureus samples with precision in ∼3 h, illustrating RAPiD as a promising new method for counting viable bacteria for microbiological applications.

In search of a counter you can count on: relative efficacy of human visual and automated colony counting

To evaluate comparative efficiency of traditional vs automated colony counting methods, cultures of Escherichia coli (ATCC 25945), Staphylococcus epidermidis (ATCC 12225), Streptococcus pyogenes (ATCC19615) and Streptococcus pneumoniae (ATCC49619) were prepared as pure cultures and mixed cultures at 0·5 McFarland standard and serial dilutions were performed. Plates were inoculated in triplicate with 50, 125, 250 and 500 colony forming units and counted by four researchers, visually and using each of the automated counters. Colony count and counting time were recorded. The pattern of efficiency for all bacterial species was similar: plates with low counts were accurate and quick to count for all methods, with an increase in time and a decrease in accuracy and precision as counts rose. Higher counts of single round colonies required less time and had greater precision with automated counters than human visual counting counts with no loss of accuracy; however, counts were reduced in accuracy and increased in time for species with less regular morphology or when plates had mixed species. Surprisingly, a free phone application was only slightly less precise and more time consuming than the high-end professional counter indicating that automation may be achievable at lower cost than expected.

SIGNIFICANCE AND IMPACT OF THE STUDY

Colony quantification is essential in clinical and research settings as well as pedagogy at the college level. Human visual (HV) counting, the most common method, is time consuming and fraught with errors. The time, accuracy and precision of HV counting were compared to a high-end professional automated counter, an inexpensive phone application and a free phone application. Low cost benefits of increased speed and accuracy with automated counting are maximized when counting single round colonies; but much reduced if colonies have irregular morphology or demonstrate haemolysis.

A new method for non-invasive biomass determination based on stereo photogrammetry

A novel, non-destructive method for the biomass estimation of biological samples on culture dishes was developed. To achieve this, a photogrammetric system, which consists of a digital single-lens reflex camera (DSLR), an illuminated platform where the culture dishes are positioned and an Arduino board which controls the capturing process, was constructed. The camera was mounted on a holder which set the camera at different title angles and the platform rotated, to capture images from different directions. A software, based on stereo photogrammetry, was developed for the three-dimensional (3D) reconstruction of the samples. The proof-of-concept was demonstrated in a series of experiments with plant tissue cultures and specifically with calli cultures of Salvia fruticosa and Ocimum basilicum. For a period of 14 days images of these cultures were acquired and 3D-reconstructions and volumetric data were obtained. The volumetric data correlated well with the experimental measurements and made the calculation of the specific growth rate, µ _max, possible. The µ _max value for S. fruticosa samples was 0.14 day^-1 and for O. basilicum 0.16 day^-1. The developed method demonstrated the high potential of this photogrammetric approach in the biological sciences.

Area-based cell colony surviving fraction evaluation: A novel fully automatic approach using general-purpose acquisition hardware

BACKGROUND

The current methodology for the Surviving Fraction (SF) measurement in clonogenic assay, which is a technique to study the anti-proliferative effect of treatments on cell cultures, involves manual counting of cell colony forming units. This procedure is operator-dependent and error-prone. Moreover, the identification of the exact colony number is often not feasible due to the high growth rate leading to the adjacent colony merging. As a matter of fact, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or the administered cytotoxic agent.

METHOD

Considering that the Area Covered by Colony (ACC) is proportional to the colony number and size as well as to the growth rate, we propose a novel fully automatic approach exploiting Circle Hough Transform, to automatically detect the wells in the plate, and local adaptive thresholding, which calculates the percentage of ACC for the SF quantification. This measurement relies just on this covering percentage and does not consider the colony number, preventing inconsistencies due to intra- and inter-operator variability.

RESULTS

To evaluate the accuracy of the proposed approach, we compared the SFs obtained by our automatic ACC-based method against the conventional counting procedure. The achieved results (r = 0.9791 and r = 0.9682 on MCF7 and MCF10A cells, respectively) showed values highly correlated with the measurements using the traditional approach based on colony number alone.

CONCLUSIONS

The proposed computer-assisted methodology could be integrated in laboratory practice as an expert system for the SF evaluation in clonogenic assays.

A smart device application for the automated determination of E. coli colonies on agar plates

The manual counting of colonies on agar plates to estimate the number of viable organisms (so-called colony-forming units-CFUs) in a defined sample is a commonly used method in microbiological laboratories. The automation of this arduous and time-consuming process through benchtop devices with integrated image processing capability addresses the need for faster and higher sample throughput and more accuracy. While benchtop colony counter solutions are often bulky and expensive, we investigated a cost-effective way to automate the colony counting process with smart devices using their inbuilt camera features and a server-based image processing algorithm. The performance of the developed solution is compared to a commercially available smartphone colony counter app and the manual counts of two scientists trained in biological experiments. The comparisons show a high accuracy of the presented system and demonstrate the potential of smart devices to displace well-established laboratory equipment.

Current status and future prospects of using advanced computer-based methods to study bacterial colonial morphology

Despite the advancement of recent molecular technologies, culturing is still considered the gold standard for microbial sample analysis. Here we review three different bacterial colony-based screening modalities that provide significant information beyond the simple shape and color of the colony. The plate imaging technique provides numeration and quantitative spectral reflectance information for each colony, while Raman spectroscopic analysis of bacteria colonies relates the Raman-shifted peaks to specific chemical bonding. Finally, the elastic-light-scatter technique provides a volumetric interaction of the whole colony through laser-bacteria interactions, instantly capturing the morphological traits of the colony and allowing quantitative classifications.