TY - JOUR

T1 - Particle Recognition on Transmission Electron Microscopy Images Using Computer Vision and Deep Learning for Catalytic Applications

AU - Nartova, Anna V.

AU - Mashukov, Mikhail Yu

AU - Astakhov, Ruslan R.

AU - Kudinov, Vitalii Yu

AU - Matveev, Andrey V.

AU - Okunev, Alexey G.

N1 - Funding Information: Funding: This work was supported by the Russian Science Foundation, project no. 22-23-00951 (https://rscf.ru/project/22-23-00951/). Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2022/2

Y1 - 2022/2

N2 - 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.

AB - 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.

KW - Deep neural networks

KW - Particle recognition

KW - Particles

KW - Supported catalysts

KW - Transmission electron microscopy

UR - http://www.scopus.com/inward/record.url?scp=85123083971&partnerID=8YFLogxK

U2 - 10.3390/catal12020135

DO - 10.3390/catal12020135

M3 - Article

AN - SCOPUS:85123083971

VL - 12

JO - Catalysts

JF - Catalysts

SN - 2073-4344

IS - 2

M1 - 135

ER -