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Deep machine learning for STEM image analysis. / Nartova, Anna V.; Matveev, Andrey V.; Kovtunova, Larisa M. et al.

In: Mendeleev Communications, Vol. 34, No. 6, 11.2024, p. 774-775.

Research output: Contribution to journal › Article › peer-review

Harvard

Nartova, AV, Matveev, AV, Kovtunova, LM & Okunev, AG 2024, 'Deep machine learning for STEM image analysis', Mendeleev Communications, vol. 34, no. 6, pp. 774-775. https://doi.org/10.1016/j.mencom.2024.10.002

APA

Nartova, A. V., Matveev, A. V., Kovtunova, L. M., & Okunev, A. G. (2024). Deep machine learning for STEM image analysis. Mendeleev Communications, 34(6), 774-775. https://doi.org/10.1016/j.mencom.2024.10.002

Vancouver

Nartova AV, Matveev AV, Kovtunova LM, Okunev AG. Deep machine learning for STEM image analysis. Mendeleev Communications. 2024 Nov;34(6):774-775. doi: 10.1016/j.mencom.2024.10.002

Author

Nartova, Anna V. ; Matveev, Andrey V. ; Kovtunova, Larisa M. et al. / Deep machine learning for STEM image analysis. In: Mendeleev Communications. 2024 ; Vol. 34, No. 6. pp. 774-775.

BibTeX

@article{e18d76da820348da8b9e094796ceb482,
title = "Deep machine learning for STEM image analysis",
abstract = "The universal, user-friendly online iOk Platform for automatic recognition of any type of objects in images based on deep machine learning is presented. Services aggregated in the iOk Platform significantly reduce the time spent on quantitative image analysis, decrease the influence of the subjective factor and increase the accuracy of the analysis by expanding the set of data that can be analyzed automatically. It is shown how the services can be used to analyze scanning transmission electron microscopy images obtained in heterogeneous catalysis studies, allowing for measurements of thousands of objects in an image, as well as simultaneous analysis of objects of different types, namely: nanoparticles and single sites.",
keywords = "STEM, automatic recognition of objects, deep machine learning, image analysis, microscopy, neural network, supported catalysts",
author = "Nartova, {Anna V.} and Matveev, {Andrey V.} and Kovtunova, {Larisa M.} and Okunev, {Aleksey G.}",
note = "This work was supported by the Ministry of Science and Higher Education of the Russian Federation within the governmental order for Boreskov Institute of Catalysis (project FWUR-2024-0032). The authors would like to thank E. Y. Gerasimov (for STEM) and Sarah Lindemann-Komarova.",
year = "2024",
month = nov,
doi = "10.1016/j.mencom.2024.10.002",
language = "English",
volume = "34",
pages = "774--775",
journal = "Mendeleev Communications",
issn = "0959-9436",
publisher = "Elsevier",
number = "6",

}

RIS

TY - JOUR

T1 - Deep machine learning for STEM image analysis

AU - Nartova, Anna V.

AU - Matveev, Andrey V.

AU - Kovtunova, Larisa M.

AU - Okunev, Aleksey G.

N1 - This work was supported by the Ministry of Science and Higher Education of the Russian Federation within the governmental order for Boreskov Institute of Catalysis (project FWUR-2024-0032). The authors would like to thank E. Y. Gerasimov (for STEM) and Sarah Lindemann-Komarova.

PY - 2024/11

Y1 - 2024/11

N2 - The universal, user-friendly online iOk Platform for automatic recognition of any type of objects in images based on deep machine learning is presented. Services aggregated in the iOk Platform significantly reduce the time spent on quantitative image analysis, decrease the influence of the subjective factor and increase the accuracy of the analysis by expanding the set of data that can be analyzed automatically. It is shown how the services can be used to analyze scanning transmission electron microscopy images obtained in heterogeneous catalysis studies, allowing for measurements of thousands of objects in an image, as well as simultaneous analysis of objects of different types, namely: nanoparticles and single sites.

AB - The universal, user-friendly online iOk Platform for automatic recognition of any type of objects in images based on deep machine learning is presented. Services aggregated in the iOk Platform significantly reduce the time spent on quantitative image analysis, decrease the influence of the subjective factor and increase the accuracy of the analysis by expanding the set of data that can be analyzed automatically. It is shown how the services can be used to analyze scanning transmission electron microscopy images obtained in heterogeneous catalysis studies, allowing for measurements of thousands of objects in an image, as well as simultaneous analysis of objects of different types, namely: nanoparticles and single sites.

KW - STEM

KW - automatic recognition of objects

KW - deep machine learning

KW - image analysis

KW - microscopy

KW - neural network

KW - supported catalysts

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85210408896&origin=inward&txGid=48d8e204f1d15838b10225298f5f6856

UR - https://www.mendeley.com/catalogue/23283ea8-380c-31db-8abd-b9900241f2d9/

U2 - 10.1016/j.mencom.2024.10.002

DO - 10.1016/j.mencom.2024.10.002

M3 - Article

VL - 34

SP - 774

EP - 775

JO - Mendeleev Communications

JF - Mendeleev Communications

SN - 0959-9436

IS - 6

ER -

ID: 61147673