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

• Published in: Microscopy and microanalysis Vol. 27; no. 3; pp. 549 - 556
• Main Authors: Groschner, Catherine K., Choi, Christina, Scott, Mary C.
• Format: Journal Article
• Language: English
• Published: New York, USA Cambridge University Press 01.06.2021; Oxford University Press
• Subjects: Automation; Cadmium selenide; Data interpretation; Datasets; Deep learning; Fault detection; High resolution; High resolution electron microscopy; Image acquisition; Image processing; Image segmentation; Image transmission; Learning algorithms; Machine learning; Morphology; Nanomaterials; Nanoparticles; Neural networks; Pipelines; Software and Instrumentation; Source code; Stacking faults; Statistical distributions; Transmission electron microscopy; deep learning; segmentation; structure classification; automated analysis; HRTEM
• ISSN: 1431-9276; 1435-8115
• DOI: 10.1017/S1431927621000386

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.