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

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 analysis of high resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from 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 detection of correlations between these features.

arXiv comment10 pages, 5 figures

References in corpus (10)

• scikit-image: Image processing in Python
• Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
• A deep learning approach to identify local structures in atomic-resolution transmission electron microscopy images
• High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel
• Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2
• A deep learning approach for determining the chiral indices of carbon nanotubes from high-resolution transmission electron microscopy images
• Automatic microscopic image analysis by moving window local Fourier Transform and Machine Learning
• Fully automated primary particle size analysis of agglomerates on transmission electron microscopy images via artificial neural networks
• Atomic mechanisms for the Si atom dynamics in graphene: chemical transformations at the edge and in the bulk
• Learning-based Defect Recognition for Quasi-Periodic Microscope Images

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• Quantifying Noise Limitations of Neural Network Segmentations in High-Resolution Transmission Electron Microscopy
• Lost Data in Electron Microscopy
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