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Open AccessArticle

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

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A.P. Ershov Institute of Informatics Systems SB RAS, Novosibirsk 630090, Russia
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Novel Software Systems LLC, Novosibirsk 630090, Russia
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State Research Center of Virology and Biotechnology “Vector” Rospotrebnadzor, Koltsovo 630559, Russia
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Grenoble Institute of Technology ENSE3, 38031 Grenoble, France
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The Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk 630090, Russia
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AcademGene LLC, Novosibirsk 630090, Russia
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St. Laurent Institute, Woburn, MA 01801, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3653; https://doi.org/10.3390/s20133653
Received: 3 June 2020 / Revised: 20 June 2020 / Accepted: 24 June 2020 / Published: 29 June 2020
(This article belongs to the Special Issue Whole-Cell Biosensors: Recent Advances)
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. View Full-Text
Keywords: neural networks; fluorescent protein-based sensors; image analysis neural networks; fluorescent protein-based sensors; image analysis
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Antonets, D.; Russkikh, N.; Sanchez, A.; Kovalenko, V.; Bairamova, E.; Shtokalo, D.; Medvedev, S.; Zakian, S. CellCountCV—A Web-Application for Accurate Cell Counting and Automated Batch Processing of Microscopic Images Using Fully Convolutional Neural Networks. Sensors 2020, 20, 3653.

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