Particle Recognition on Transmission Electron Microscopy Images Using Computer Vision and Deep Learning for Catalytic Applications

Recognition and measuring particles on microscopy images is an important part of many scientific studies, including catalytic investigations. In this paper, we present the results of the application of deep learning to the automated recognition of nanoparticles deposited on porous supports (heteroge...

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Bibliographic Details
Published in:Catalysts Vol. 12; no. 2; p. 135
Main Authors: Nartova, Anna V., Mashukov, Mikhail Yu, Astakhov, Ruslan R., Kudinov, Vitalii Yu, Matveev, Andrey V., Okunev, Alexey G.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.02.2022
Subjects:
Artificial intelligence
Artificial neural networks
Catalysis
Catalysts
Chemical reactions
Computer vision
Data processing
Datasets
Deep learning
Image analysis
Image transmission
Materials science
Nanoparticles
Neural networks
Object recognition
Particle size
Particle size determination
Polymers
Recall
Scanning electron microscopy
Size determination
Software
Statistical analysis
Transmission electron microscopy
Web services
ISSN:2073-4344, 2073-4344
Online Access:Get full text
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Summary:Recognition and measuring particles on microscopy images is an important part of many scientific studies, including catalytic investigations. In this paper, we present the results of the application of deep learning to the automated recognition of nanoparticles deposited on porous supports (heterogeneous catalysts) on images obtained by transmission electron microscopy (TEM). The Cascade Mask-RCNN neural network was used. During the training, two types of objects were labeled on raw TEM images of ‘real’ catalysts: visible particles and overlapping particle projections. The trained neural network recognized nanoparticles in the test dataset with 0.71 precision and 0.72 recall for both classes of objects and 0.84 precision and 0.79 recall for visible particles. The developed model is integrated into the open-access web service ‘ParticlesNN’, which can be used by any researcher in the world. Instead of hours, TEM data processing per one image analysis is reduced to a maximum of a couple of minutes and the divergence of mean particle size determination is approximately 2% compared to manual analysis. The proposed tool encourages accelerating catalytic research and improving the objectivity and accuracy of analysis.
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ISSN:2073-4344
2073-4344
DOI:10.3390/catal12020135