Machine Learning Pipeline for Segmentation and Defect Identification from High-Resolution Transmission Electron Microscopy Data
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Machine Learning Pipeline for Segmentation and Defect Identification from High-Resolution Transmission Electron Microscopy Data
Authors
Keywords
-
Journal
MICROSCOPY AND MICROANALYSIS
Volume -, Issue -, Pages 1-8
Publisher
Cambridge University Press (CUP)
Online
2021-05-06
DOI
10.1017/s1431927621000386
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Learning-based defect recognition for quasi-periodic HRSTEM images
- (2021) Nik Dennler et al. MICRON
- A deep learning approach for determining the chiral indices of carbon nanotubes from high-resolution transmission electron microscopy images
- (2020) G.D. Förster et al. CARBON
- Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2
- (2019) Artem Maksov et al. npj Computational Materials
- High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel
- (2019) Brian L. DeCost et al. MICROSCOPY AND MICROANALYSIS
- Software tools for automated transmission electron microscopy
- (2019) Martin Schorb et al. NATURE METHODS
- Lab on a beam—Big data and artificial intelligence in scanning transmission electron microscopy
- (2019) Sergei V. Kalinin et al. MRS BULLETIN
- Using Neural Networks to Identify Atoms in HRTEM Images
- (2019) Jakob Schiøtz et al. MICROSCOPY AND MICROANALYSIS
- Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk
- (2019) Maxim Ziatdinov et al. ADVANCED FUNCTIONAL MATERIALS
- Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels
- (2019) Graham Roberts et al. Scientific Reports
- Automatic microscopic image analysis by moving window local Fourier Transform and Machine Learning
- (2019) Benedykt R. Jany et al. MICRON
- Fully automated primary particle size analysis of agglomerates on transmission electron microscopy images via artificial neural networks
- (2018) Max Frei et al. POWDER TECHNOLOGY
- Automatic Segmentation of Inorganic Nanoparticles in BF TEM Micrographs
- (2018) D.J. Groom et al. ULTRAMICROSCOPY
- Influence of Atomic-Level Morphology on Catalysis: The Case of Sphere and Rod-Like Gold Nanoclusters for CO2 Electroreduction
- (2018) Shuo Zhao et al. ACS Catalysis
- Automated defect analysis in electron microscopic images
- (2018) Wei Li et al. npj Computational Materials
- Identifying Atoms in High Resolution Transmission Electron Micrographs Using a Deep Convolutional Neural Net
- (2018) Jakob Schiøtz et al. MICROSCOPY AND MICROANALYSIS
- High-Throughput, Algorithmic Determination of Nanoparticle Structure from Electron Microscopy Images
- (2015) Christine R. Laramy et al. ACS Nano
- Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool
- (2015) Abdel Aziz Taha et al. BMC MEDICAL IMAGING
- Correlation of Atomic Structure and Photoluminescence of the Same Quantum Dot: Pinpointing Surface and Internal Defects That Inhibit Photoluminescence
- (2014) Noah J. Orfield et al. ACS Nano
- scikit-image: image processing in Python
- (2014) Stéfan van der Walt et al. PeerJ
- Measurement of the intrinsic strength of crystalline and polycrystalline graphene
- (2013) Haider I. Rasool et al. Nature Communications
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started