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

Zhu, Guozhen; Yan, Bin; Xing, Mengting; Tian, Chunna

doi:10.1016/j.mimet.2018.09.004

Cited by 23 publications

(10 citation statements)

References 22 publications

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“…There is a growing interest in applying computer vision algorithms to microbiological analyses, especially in industry and pharmaceutical branches, but many existing approaches are based on traditional image processing techniques [32][33][34][35][36] . Although deep learning methods have recently become more commonly used 17,[37][38][39][40][41] , the proposed procedures are usually not end-to-end solutions and cannot be used independently to process an image of an entire Petri plate.…”

Section: Discussionmentioning

confidence: 99%

AGAR a Microbial Colony Dataset for Deep Learning Detection

Majchrowska¹,

Pawłowski²,

Guła

et al. 2021

Preprint

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The Annotated Germs for Automated Recognition (AGAR) dataset is an image database of microbial colonies cultured on agar plates. It contains 18 000 photos of five different microorganisms as single or mixed cultures, taken under diverse lighting conditions with two different cameras. All the images are classified into countable, uncountable, and empty, with the countable class labeled by microbiologists with colony location and species identification (336 442 colonies in total). This study describes the dataset itself and the process of its development. In the second part, the performance of selected deep neural network architectures for object detection, namely Faster R-CNN and Cascade R-CNN, was evaluated on the AGAR dataset. The results confirmed the great potential of deep learning methods to automate the process of microbe localization and classification based on Petri dish photos. Moreover, AGAR is the first publicly available dataset of this kind and size and will facilitate the future development of machine learning models. The data used in these studies can be found at https://agar.neurosys.com/.

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Section: Discussionmentioning

confidence: 99%

AGAR a Microbial Colony Dataset for Deep Learning Detection

Majchrowska¹,

Pawłowski²,

Guła

et al. 2021

Preprint

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“…In Zhu et al. ( 2018 ), image subtraction is carried out to extract the candidate colonies, which are connected to the inner circle of the agar plate and a nonlinear gray transformation is used to enhance the gray-scale. Afterward, a distance transformation is performed on the binarized image and segmentation is done with a watershed transformation.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

“…The total number of single and clustered colonies is counted with an average relative error of 0.2% in Zhu et al. ( 2018 ). The counting result is shown in Fig.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

“…( 2019 ) Bacteria Multidirectional Sobel operator Otsu thresholding, circularity filter and inertia filter 99.8% accuracy for high definition dataset and 95.9% for low definition dataset Zhu et al. ( 2018 ) Bacteria Nonliner gray transform Image subtraction and watershed 99.8% accuracy Tamiev et al. ( 2020 ) Bacteria Adaptive thresholding Classification-type convolutional neural network David and Paul ( 1989 ) Bacteria Image enhancement Model 2000 (Image Technology Corporation, Deer Park, New York) image analysis system Kildesø and Nielsen ( 1997 ) Bacteria Gaussian filter and Laplacian filter Kontron Vidas Plus (Kontron Elektronik GmbH, Germany) image analyser system Shopov et al.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

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

Zhang

Rahaman

et al. 2021

Artif Intell Rev

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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.

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“…Counting the number of colonies is relatively simple to automate, and several algorithms have been published (9)(10)(11)(12)(13)(14)(15) and some are included in the software for commercial equipment (16). Image segmentation methods are diverse and many different approaches have been implemented.…”

Section: Introductionmentioning

confidence: 99%

ColFeatures: Automated data extraction and classification of bacterial colonies

García‐Soriano

et al. 2021

Preprint

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Examining microbial colonies on agar plates have been at the core of microbiology for many decades. It is usually done manually, and therefore subject to bias besides requiring a considerable amount of time and effort. In order to optimize and standardize the identification of bacterial colonies growing on agar plates, we have developed an open access tool available on GitHub: ColFeatures. The software allows automated identification of bacterial colonies, extracts key morphological data and generate labels that ensure tracking of temporal development. We included machine learning algorithms that provide sorting of environmental isolates by using cluster methodologies. Furthermore, we show how cluster performance is evaluated using index scores (Silhouette, Calinski-Harabasz, Davies-Bouldin) to ensure the outcome of colony classification. As automation becomes more prominent in microbiology, tools as ColFeatures will assist identification of bacterial colonies on agar plates, bypassing human bias and complementing sequencing or mass spectrometry information that often comes attached with a considerable price tag.

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

Cited by 23 publications

References 22 publications

AGAR a Microbial Colony Dataset for Deep Learning Detection

AGAR a Microbial Colony Dataset for Deep Learning Detection

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

ColFeatures: Automated data extraction and classification of bacterial colonies

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