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Machine Learning Pipeline for Segmentation and Defect Identification from High-Resolution Transmission Electron Microscopy Data

Published online by Cambridge University Press:  06 May 2021

Catherine K. Groschner ,
Christina Choi  and
Mary C. Scott
Catherine K. Groschner
Affiliation:
Department of Materials Science and Engineering, University of California Berkeley, Berkeley, CA94720, USA
Christina Choi
Affiliation:
Department of Materials Science and Engineering, University of California Berkeley, Berkeley, CA94720, USA
Mary C. Scott*
Affiliation:
Department of Materials Science and Engineering, University of California Berkeley, Berkeley, CA94720, USA Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA94720, USA
*
*Author for correspondence: Mary C. Scott, E-mail: [email protected]
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Abstract

In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach for fast, accurate analysis of electron microscopy data. Here, we demonstrate a flexible two-step pipeline for the analysis of high-resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for the detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from the amorphous background with a Dice coefficient of 0.8 and significantly outperforms traditional image segmentation methods. Using these segmented regions, we are then able to classify whether nanoparticles contain a visible stacking fault with 86% accuracy. We provide this adaptable pipeline as an open-source tool for the community. The combined output of the segmentation network and classifier offer a way to determine statistical distributions of features of interest, such as size, shape, and defect presence, enabling the detection of correlations between these features.

Keywords

automated analysisdeep learningHRTEMsegmentationstructure classification
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 27 , Issue 3 , June 2021 , pp. 549 - 556
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

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