Automated classification of bacterial cell sub-populations with convolutional neural networks

Tamiev, Denis; Furman, Paige; Reuel, Nigel F.

doi:10.1371/journal.pone.0241200

Cited by 16 publications

(13 citation statements)

References 30 publications

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“…( 2015 , 2017 ); Tamiev et al. ( 2020 ), the convolutional neural network (CNN) is applied for bacterial colony counting. The example of the dataset is shown in Fig.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

“…In Tamiev et al. ( 2020 ), a classification-type convolutional neural network (cCNN) is proposed for automatic bacteria classification and counting, and an efficient method for microscope image preprocessing is presented. First, the raw images are segmented with an adaptive binary thresholding method and images with individual cells or cell clusters are cropped.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

“…( 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. ( 2000 ) Bacteria Marr-Hildreth Gaussian-smoothed Laplacian edge-detection protocol, flat-fielding and edge-strength operators Skidaway Tools (Skidaway Institute of Oceanography, 10 Ocean Science Circle, Savannah, USA) Gmür et al.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

“…In Tamiev et al. ( 2020 ), a classification-type convolutional neural network (cCNN) is designed for bacteria classification and counting.CNN can automatically extract the features of images and process high-dimensional data quickly, but the pooling layer may lose much valuable information while training.

Fig.…”

Section: Analysis Of Image Processing Based Counting Methodsmentioning

confidence: 99%

“…( 2017 ) CNN Nvidia Titan X 50000 iterations in 1 hours – 92.1% Tamiev et al. ( 2020 ) cCNN NVIDIA Quadro K620 – 3.8 times faster 86% Albaradei et al. ( 2020 ) SRNet – – – RMSE = 22.38 …”

Section: Analysis Of Image Processing Based 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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“…( 2015 , 2017 ); Tamiev et al. ( 2020 ), the convolutional neural network (CNN) is applied for bacterial colony counting. The example of the dataset is shown in Fig.…”

Section: Bacteria Counting Methodsmentioning

confidence: 99%

Section: Bacteria Counting Methodsmentioning

confidence: 99%

Section: Bacteria Counting Methodsmentioning

confidence: 99%

Fig.…”

Section: Analysis Of Image Processing Based Counting Methodsmentioning

confidence: 99%

Section: Analysis Of Image Processing Based Counting Methodsmentioning

confidence: 99%

See 3 more Smart Citations

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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show abstract

Reinforcement learning‐guided control strategies for CAR T‐cell activation and expansion

Ferdous,

Shihab,

Chowdhury

et al. 2024

Biotech & Bioengineering

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Reinforcement learning (RL), a subset of machine learning (ML), could optimize and control biomanufacturing processes, such as improved production of therapeutic cells. Here, the process of CAR T‐cell activation by antigen‐presenting beads and their subsequent expansion is formulated in silico. The simulation is used as an environment to train RL‐agents to dynamically control the number of beads in culture to maximize the population of robust effector cells at the end of the culture. We make periodic decisions of incremental bead addition or complete removal. The simulation is designed to operate in OpenAI Gym, enabling testing of different environments, cell types, RL‐agent algorithms, and state inputs to the RL‐agent. RL‐agent training is demonstrated with three different algorithms (PPO, A2C, and DQN), each sampling three different state input types (tabular, image, mixed); PPO‐tabular performs best for this simulation environment. Using this approach, training of the RL‐agent on different cell types is demonstrated, resulting in unique control strategies for each type. Sensitivity to input‐noise (sensor performance), number of control step interventions, and advantages of pre‐trained RL‐agents are also evaluated. Therefore, we present an RL framework to maximize the population of robust effector cells in CAR T‐cell therapy production.

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Automated Counting via Multicolumn Network and CytoSMART Exact FL Microscope

Flórez

González‐Briones

Hernández

et al. 2023

Lecture Notes in Networks and Systems

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Automated classification of bacterial cell sub-populations with convolutional neural networks

Cited by 16 publications

References 30 publications

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

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

Reinforcement learning‐guided control strategies for CAR T‐cell activation and expansion

Automated Counting via Multicolumn Network and CytoSMART Exact FL Microscope

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