Comparison of two automatic cell-counting solutions for fluorescent microscopic images

2015 ◽  
Vol 260 (1) ◽  
pp. 107-116 ◽  
Author(s):  
J. LOJK ◽  
U. ČIBEJ ◽  
D. KARLAŠ ◽  
L. ŠAJN ◽  
M. PAVLIN
Keyword(s):  
Cell Counting ◽  
Microscopic Images ◽  
Counting Solutions

scholarly journals Automated Counting of Cancer Cells by Ensembling Deep Features

Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1019 ◽  
Author(s):  
Liu ◽  
Junker ◽  
Murakami ◽  
Hu
Keyword(s):  
High Throughput ◽  
Pearson Correlation ◽  
Image Features ◽  
Cell Counting ◽  
Single Type ◽  
Imaging Features ◽  
Neural Network Models ◽  
Regression Problem ◽  
Microscopic Images ◽  
Cell Counts

High-content and high-throughput digital microscopes have generated large image sets in biological experiments and clinical practice. Automatic image analysis techniques, such as cell counting, are in high demand. Here, cell counting was treated as a regression problem using image features (phenotypes) extracted by deep learning models. Three deep convolutional neural network models were developed to regress image features to their cell counts in an end-to-end way. Theoretically, ensembling imaging phenotypes should have better representative ability than a single type of imaging phenotype. We implemented this idea by integrating two types of imaging phenotypes (dot density map and foreground mask) extracted by two autoencoders and regressing the ensembled imaging phenotypes to cell counts afterwards. Two publicly available datasets with synthetic microscopic images were used to train and test the proposed models. Root mean square error, mean absolute error, mean absolute percent error, and Pearson correlation were applied to evaluate the models’ performance. The well-trained models were also applied to predict the cancer cell counts of real microscopic images acquired in a biological experiment to evaluate the roles of two colorectal-cancer-related genes. The proposed model by ensembling deep imaging features showed better performance in terms of smaller errors and larger correlations than those based on a single type of imaging feature. Overall, all models’ predictions showed a high correlation with the true cell counts. The ensembling-based model integrated high-level imaging phenotypes to improve the estimation of cell counts from high-content and high-throughput microscopic images.

scholarly journals CellCountCV—A Web-Application for Accurate Cell Counting and Automated Batch Processing of Microscopic Images Using Fully Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3653
Author(s):  
Denis Antonets ◽  
Nikolai Russkikh ◽  
Antoine Sanchez ◽  
Victoria Kovalenko ◽  
Elvira Bairamova ◽  
...  
Keyword(s):  
Neural Networks ◽  
Endoplasmic Reticulum ◽  
Endoplasmic Reticulum Stress ◽  
Web Application ◽  
Experimental Studies ◽  
Living Cells ◽  
Cell Counting ◽  
Microscopic Images ◽  
Cellular Models ◽  
Cell Counts

In vitro cellular models are promising tools for studying normal and pathological conditions. One of their important applications is the development of genetically engineered biosensor systems to investigate, in real time, the processes occurring in living cells. At present, there are fluorescence, protein-based, sensory systems for detecting various substances in living cells (for example, hydrogen peroxide, ATP, Ca2+ etc.,) or for detecting processes such as endoplasmic reticulum stress. Such systems help to study the mechanisms underlying the pathogenic processes and diseases and to screen for potential therapeutic compounds. It is also necessary to develop new tools for the processing and analysis of obtained microimages. Here, we present our web-application CellCountCV for automation of microscopic cell images analysis, which is based on fully convolutional deep neural networks. This approach can efficiently deal with non-convex overlapping objects, that are virtually inseparable with conventional image processing methods. The cell counts predicted with CellCountCV were very close to expert estimates (the average error rate was < 4%). CellCountCV was used to analyze large series of microscopic images obtained in experimental studies and it was able to demonstrate endoplasmic reticulum stress development and to catch the dose-dependent effect of tunicamycin.

Application of Biomedical Image Processing in Blood Cell Counting using Hough Transform

2016 ◽  
pp. 359-378 ◽  
Author(s):  
Manali Mukherjee ◽  
Kamarujjaman ◽  
Mausumi Maitra
Keyword(s):  
Blood Cell ◽  
Blood Cells ◽  
Complete Blood Count ◽  
White Blood Cells ◽  
Blood Stream ◽  
Cell Counting ◽  
Biomedical Image Processing ◽  
Microscopic Images ◽  
Biomedical Image ◽  
Rapid Heart Rate

In the field of biomedicine, blood cells are complex in nature. Nowadays, microscopic images are used in several laboratories for detecting cells or parasite by technician. The microscopic images of a blood stream contain RBCs, WBCs and Platelets. Blood cells are produced in the bone marrow and regularly released into circulation. Blood counts are monitored with a laboratory test called a Complete Blood Count (CBC). However, certain circumstances may cause to have fewer cells than is considered normal, a condition which is called “low blood counts”.This can be accomplished with the administration of blood cell growth factors. Common symptoms due to low red blood cells are:fatigue or tiredness, trouble breathing, rapid heart rate, difficulty staying warm, pale skin etc. Common symptoms due to low white blood cells are: infection, fever etc. It is important to monitor for low blood cell count because conditions could increase the risk of unpleasant and sometimes life-threatening side effects.

scholarly journals Fast automated yeast cell counting algorithm using bright-field and fluorescence microscopic images

2013 ◽  
Vol 15 (1) ◽  
pp. 13 ◽  
Author(s):  
Dongpyo Hong ◽  
Gwanghee Lee ◽  
Neon Cheol Jung ◽  
Moongu Jeon
Keyword(s):  
Yeast Cell ◽  
Cell Counting ◽  
Bright Field ◽  
Microscopic Images ◽  
Counting Algorithm

scholarly journals Fast and Simple Tool for the Quantification of Biofilm-Embedded Cells Sub-Populations from Fluorescent Microscopic Images

2017 ◽  
Author(s):  
Mikhail I Bogachev ◽  
Vladimir Yu Volkov ◽  
Oleg A Markelov ◽  
Elena Yu Trizna ◽  
Diana R Baydamshina ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cells ◽  
Software Tool ◽  
Laser Scanning Microscopy ◽  
Cell Counting ◽  
Eukaryotic Cells ◽  
Distribution Analysis ◽  
Cell Size Distribution ◽  
Suggested Approach ◽  
Microscopic Images

AbstractFluorescent staining is a common tool for both quantitative and qualitative assessment of pro- and eukaryotic cells sub-population fractions by using microscopy and flow cytometry. However, direct cell counting by flow cytometry is often limited, for example when working with cells rigidly adhered either to each other or to external surfaces like bacterial biofilms or adherent cell lines and tissue samples. An alternative approach is provided by using fluorescent microscopy and confocal laser scanning microscopy (CLSM), which enables the evaluation of fractions of cells subpopulations in a given sample. For the quantitative assessment of cell fractions in microphotographs, we suggest a simple two-step algorithm that combines single cells selection and the statistical analysis. To facilitate the first step, we suggest a simple procedure that supports finding the balance between the detection threshold and the typical size of single cells based on objective cell size distribution analysis. Based on a series of experimental measurements performed on bacterial and eukaryotic cells under various conditions, we show explicitly that the suggested approach effectively accounts for the fractions of different cell sub-populations (like the live/dead staining in our samples) in all studied cases that are in good agreement with manual cell counting on microphotographs and flow cytometry data. This algorithm is implemented as a simple software tool that includes an intuitive and user-friendly graphical interface for the initial adjustment of algorithm parameters to the microphotographs analysis as well as for the sequential analysis of homogeneous series of similar microscopic images without further user intervention. The software tool entitled BioFilmAnalyzer is freely available online at https://bitbucket.org/rogex/biofilmanalyzer/downloads/.

scholarly journals Efficient Detection of Longitudinal Bacteria Fission Using Transfer Learning in Deep Neural Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Carlos Garcia-Perez ◽  
Keiichi Ito ◽  
Javier Geijo ◽  
Roman Feldbauer ◽  
Nico Schreiber ◽  
...  
Keyword(s):  
Cell Division ◽  
Transfer Learning ◽  
Binary Classification ◽  
Classification Model ◽  
Cell Counting ◽  
Cellular Division ◽  
Microscopic Images ◽  
Efficient Detection ◽  
Longitudinal Division

A very common way to classify bacteria is through microscopic images. Microscopic cell counting is a widely used technique to measure microbial growth. To date, fully automated methodologies are available for accurate and fast measurements; yet for bacteria dividing longitudinally, as in the case of Candidatus Thiosymbion oneisti, its cell count mainly remains manual. The identification of this type of cell division is important because it helps to detect undergoing cellular division from those which are not dividing once the sample is fixed. Our solution automates the classification of longitudinal division by using a machine learning method called residual network. Using transfer learning, we train a binary classification model in fewer epochs compared to the model trained without it. This potentially eliminates most of the manual labor of classifying the type of bacteria cell division. The approach is useful in automatically labeling a certain bacteria division after detecting and segmenting (extracting) individual bacteria images from microscopic images of colonies.

Application of Biomedical Image Processing in Blood Cell Counting Using Hough Transform

Biometrics ◽  
2017 ◽  
pp. 1175-1194
Author(s):  
Manali Mukherjee ◽  
Kamarujjaman ◽  
Mausumi Maitra
Keyword(s):  
Blood Cell ◽  
Blood Cells ◽  
Complete Blood Count ◽  
White Blood Cells ◽  
Blood Stream ◽  
Cell Counting ◽  
Biomedical Image Processing ◽  
Microscopic Images ◽  
Biomedical Image ◽  
Rapid Heart Rate

In the field of biomedicine, blood cells are complex in nature. Nowadays, microscopic images are used in several laboratories for detecting cells or parasite by technician. The microscopic images of a blood stream contain RBCs, WBCs and Platelets. Blood cells are produced in the bone marrow and regularly released into circulation. Blood counts are monitored with a laboratory test called a Complete Blood Count (CBC). However, certain circumstances may cause to have fewer cells than is considered normal, a condition which is called “low blood counts”. This can be accomplished with the administration of blood cell growth factors. Common symptoms due to low red blood cells are: fatigue or tiredness, trouble breathing, rapid heart rate, difficulty staying warm, pale skin etc. Common symptoms due to low white blood cells are: infection, fever etc. It is important to monitor for low blood cell count because conditions could increase the risk of unpleasant and sometimes life-threatening side effects.

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