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Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning Full article

Journal Nanomaterials
, E-ISSN: 2079-4991
Output data Year: 2020, Volume: 10, Number: 7, Article number : 1285, Pages count : 16 DOI: 10.3390/nano10071285
Tags particle recognition; deep neural networks; scanning tunneling microscopy; particles
Authors Okunev Alexey G. 1,2 , Mashukov Mikhail Yu. 3 , Nartova Anna V. 2,3 , Matveev Andrey V. 2,3
Affiliations
1 Novosibirsk State University Higher College of Informatics, Russkaja Str. 35, 630058 Novosibirsk, Russia
2 Boreskov Institute of Catalysis SB RAS, pr. Acad. Lavrentieva, 5, 630090 Novosibirsk, Russia
3 Scientific-Educational Center “Machine Learning and Big Data Analysis”, Novosibirsk State University, Pirogova Str. 1, 630090 Novosibirsk, Russia

Funding (1)

1 Federal Agency for Scientific Organizations 0303-2016-0001

Abstract: Identifying, counting and measuring particles is an important component of many research studies. Images with particles are usually processed by hand using a software ruler. Automated processing, based on conventional image processing methods (edge detection, segmentation, etc.) are not universal, can only be used on good-quality images and need to set a number of parameters empirically. In this paper, we present results from the application of deep learning to automated recognition of metal nanoparticles deposited on highly oriented pyrolytic graphite on images obtained by scanning tunneling microscopy (STM). We used the Cascade Mask-RCNN neural network. Training was performed on a data set containing 23 STM images with 5157 nanoparticles. Three images containing 695 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in the verification set with 0.93 precision and 0.78 recall. Predicted contour refining with 2D Gaussian function was a proposed option. The accuracies for mean particle size calculated from predicted contours compared with ground truth were in the range of 0.87–0.99. The results were compared with outcomes from other generally available software, based on conventional image processing methods. The advantages of deep learning methods for automatic particle recognition were clearly demonstrated. We developed a free open-access web service “ParticlesNN” based on the trained neural network, which can be used by any researcher in the world.
Cite: Okunev A.G. , Mashukov M.Y. , Nartova A.V. , Matveev A.V.
Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning
Nanomaterials. 2020. V.10. N7. 1285 :1-16. DOI: 10.3390/nano10071285 WOS Scopus РИНЦ AN PMID OpenAlex
Files: Full text from publisher
Dates:
Submitted: Jun 11, 2020
Accepted: Jun 26, 2020
Published online: Jun 30, 2020
Published print: Jul 1, 2020
Identifiers:
Web of science: WOS:000556472600001
Scopus: 2-s2.0-85087371596
Elibrary: 43303223
Chemical Abstracts: 2020:1587336
PMID: 32629955
OpenAlex: W3039460860
Citing:
DB Citing
Scopus 59
Elibrary 47
Web of science 50
OpenAlex 64
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