Recognition of tenogenic differentiation using convolutional neural network

Methodologies to assess stem cell differentiation in the culturing state are needed for regenerative medicine and tissue engineering techniques. In recent years, convolutional neural networks (CNNs), a class of deep neural networks, have made impressive advancements in image-based classification, re...

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Autores principales:	Dursun Gözde, Balkrishna Tandale Saurabh, Eschweiler Jörg, Tohidnezhad Mersedeh, Markert Bernd, Stoffel Marcus
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	stem cell differentiation cell classification convolutional neural network Medicine R
Acceso en línea:	https://doaj.org/article/a91a5e65f8314e5ca36f9925f0696e7a
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id	oai:doaj.org-article:a91a5e65f8314e5ca36f9925f0696e7a
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spelling	oai:doaj.org-article:a91a5e65f8314e5ca36f9925f0696e7a2021-12-05T14:10:42ZRecognition of tenogenic differentiation using convolutional neural network2364-550410.1515/cdbme-2020-3051https://doaj.org/article/a91a5e65f8314e5ca36f9925f0696e7a2020-09-01T00:00:00Zhttps://doi.org/10.1515/cdbme-2020-3051https://doaj.org/toc/2364-5504Methodologies to assess stem cell differentiation in the culturing state are needed for regenerative medicine and tissue engineering techniques. In recent years, convolutional neural networks (CNNs), a class of deep neural networks, have made impressive advancements in image-based classification, recognition and detection tasks. CNNs have been introduced as a non-invasive cell characterization method by learning features directly from image data of unlabeled cells. Furthermore, this approach serves as a rapid and inexpensive methodology with high performance compared to traditional techniques that require complex laboratory procedures including antibody staining and gene expression analysis. Here, we studied the potential of the CNNs approach to recognize stem cell differentiation based on cell morphology utilizing phasecontrast microscopy images.We have examined the differentiation potential of bone marrow mesenchymal stem cells (BMSCs) into tenocytes, with the treatment of bone morphogenetic protein-12 (BMP-12). After treatment, the phase-contrast images of cells were obtained directly from cell culture flasks to train CNN and the differentiated phenotype of stem cells was characterized by immunostaining. CNN was able to classify the cells into three groups including non-stem cells (chondrocytes), stem cells (BMSCs) and differentiated stem cells (tenocytes) based on their morphology with 92.2 % accuracy. The presented study revealed that CNN performed faster and non-invasive cell classification task compared to traditional methodologies.Dursun GözdeBalkrishna Tandale SaurabhEschweiler JörgTohidnezhad MersedehMarkert BerndStoffel MarcusDe Gruyterarticlestem cell differentiationcell classificationconvolutional neural networkMedicineRENCurrent Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 200-204 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	stem cell differentiation cell classification convolutional neural network Medicine R
spellingShingle	stem cell differentiation cell classification convolutional neural network Medicine R Dursun Gözde Balkrishna Tandale Saurabh Eschweiler Jörg Tohidnezhad Mersedeh Markert Bernd Stoffel Marcus Recognition of tenogenic differentiation using convolutional neural network
description	Methodologies to assess stem cell differentiation in the culturing state are needed for regenerative medicine and tissue engineering techniques. In recent years, convolutional neural networks (CNNs), a class of deep neural networks, have made impressive advancements in image-based classification, recognition and detection tasks. CNNs have been introduced as a non-invasive cell characterization method by learning features directly from image data of unlabeled cells. Furthermore, this approach serves as a rapid and inexpensive methodology with high performance compared to traditional techniques that require complex laboratory procedures including antibody staining and gene expression analysis. Here, we studied the potential of the CNNs approach to recognize stem cell differentiation based on cell morphology utilizing phasecontrast microscopy images.We have examined the differentiation potential of bone marrow mesenchymal stem cells (BMSCs) into tenocytes, with the treatment of bone morphogenetic protein-12 (BMP-12). After treatment, the phase-contrast images of cells were obtained directly from cell culture flasks to train CNN and the differentiated phenotype of stem cells was characterized by immunostaining. CNN was able to classify the cells into three groups including non-stem cells (chondrocytes), stem cells (BMSCs) and differentiated stem cells (tenocytes) based on their morphology with 92.2 % accuracy. The presented study revealed that CNN performed faster and non-invasive cell classification task compared to traditional methodologies.
format	article
author	Dursun Gözde Balkrishna Tandale Saurabh Eschweiler Jörg Tohidnezhad Mersedeh Markert Bernd Stoffel Marcus
author_facet	Dursun Gözde Balkrishna Tandale Saurabh Eschweiler Jörg Tohidnezhad Mersedeh Markert Bernd Stoffel Marcus
author_sort	Dursun Gözde
title	Recognition of tenogenic differentiation using convolutional neural network
title_short	Recognition of tenogenic differentiation using convolutional neural network
title_full	Recognition of tenogenic differentiation using convolutional neural network
title_fullStr	Recognition of tenogenic differentiation using convolutional neural network
title_full_unstemmed	Recognition of tenogenic differentiation using convolutional neural network
title_sort	recognition of tenogenic differentiation using convolutional neural network
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/a91a5e65f8314e5ca36f9925f0696e7a
work_keys_str_mv	AT dursungozde recognitionoftenogenicdifferentiationusingconvolutionalneuralnetwork AT balkrishnatandalesaurabh recognitionoftenogenicdifferentiationusingconvolutionalneuralnetwork AT eschweilerjorg recognitionoftenogenicdifferentiationusingconvolutionalneuralnetwork AT tohidnezhadmersedeh recognitionoftenogenicdifferentiationusingconvolutionalneuralnetwork AT markertbernd recognitionoftenogenicdifferentiationusingconvolutionalneuralnetwork AT stoffelmarcus recognitionoftenogenicdifferentiationusingconvolutionalneuralnetwork
_version_	1718371801590923264

Recognition of tenogenic differentiation using convolutional neural network

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