skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning

Abstract

Highlights: • A unique automated segmentation approach for large 3D nanotomography datasets obtained by Transmission X-ray Microscopy (TXM) in an Al-Cu alloy. • Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. • This extremely versatile technique is capable of emulating the manual segmentation process effectively. • Quantitative comparison between manual segmentation and CNN architecture established the accuracy of this automated technique. • Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis. - Abstract: A unique correlative approach for automated segmentation of large 3D nanotomography datasets obtained using Transmission X-ray Microscopy (TXM) in an Al-Cu alloy has been introduced. Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. This extremely versatile technique is capable of emulating the manual segmentation process effectively. Coupling this technique with post-scanning SEM imaging ensured precise estimation of 3D morphological parameters from nanotomography. The segmentation process as well as subsequent analysis was expedited by several orders of magnitude. Quantitative comparison between segmentation performed manually and using the CNN architecture established the accuracy of this automated technique. Its ability tomore » robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis, possibly opening up novel avenues for performing 4D characterization experiments with finer time steps.« less

Authors:
 [1]; ; ;  [2];  [3];  [2];  [1]
  1. Center for 4D Materials Science, Arizona State University, Tempe, AZ 85287-6106 (United States)
  2. Advanced Photon Source, Argonne National Laboratory, Building 401, 9700 S. Cass Avenue, Argonne, IL 60439 (United States)
  3. Argonne Leadership Computing Facility, Argonne National Laboratory, Building 401, 9700 S. Cass Avenue, Argonne, IL 60439 (United States)
Publication Date:
OSTI Identifier:
22805091
Resource Type:
Journal Article
Journal Name:
Materials Characterization
Additional Journal Information:
Journal Volume: 142; Other Information: Copyright (c) 2017 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 1044-5803
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; ALUMINIUM ALLOYS; COMPUTERIZED SIMULATION; COMPUTERIZED TOMOGRAPHY; DATA ANALYSIS; NEURAL NETWORKS; PRECIPITATION; SCANNING ELECTRON MICROSCOPY; X RADIATION

Citation Formats

Shashank Kaira, C., Yang, Xiaogang, De Andrade, Vincent, De Carlo, Francesco, Scullin, William, Gursoy, Doga, and Chawla, Nikhilesh. Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning. United States: N. p., 2018. Web. doi:10.1016/J.MATCHAR.2018.05.053.
Shashank Kaira, C., Yang, Xiaogang, De Andrade, Vincent, De Carlo, Francesco, Scullin, William, Gursoy, Doga, & Chawla, Nikhilesh. Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning. United States. https://doi.org/10.1016/J.MATCHAR.2018.05.053
Shashank Kaira, C., Yang, Xiaogang, De Andrade, Vincent, De Carlo, Francesco, Scullin, William, Gursoy, Doga, and Chawla, Nikhilesh. Wed . "Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning". United States. https://doi.org/10.1016/J.MATCHAR.2018.05.053.
@article{osti_22805091,
title = {Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning},
author = {Shashank Kaira, C. and Yang, Xiaogang and De Andrade, Vincent and De Carlo, Francesco and Scullin, William and Gursoy, Doga and Chawla, Nikhilesh},
abstractNote = {Highlights: • A unique automated segmentation approach for large 3D nanotomography datasets obtained by Transmission X-ray Microscopy (TXM) in an Al-Cu alloy. • Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. • This extremely versatile technique is capable of emulating the manual segmentation process effectively. • Quantitative comparison between manual segmentation and CNN architecture established the accuracy of this automated technique. • Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis. - Abstract: A unique correlative approach for automated segmentation of large 3D nanotomography datasets obtained using Transmission X-ray Microscopy (TXM) in an Al-Cu alloy has been introduced. Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. This extremely versatile technique is capable of emulating the manual segmentation process effectively. Coupling this technique with post-scanning SEM imaging ensured precise estimation of 3D morphological parameters from nanotomography. The segmentation process as well as subsequent analysis was expedited by several orders of magnitude. Quantitative comparison between segmentation performed manually and using the CNN architecture established the accuracy of this automated technique. Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis, possibly opening up novel avenues for performing 4D characterization experiments with finer time steps.},
doi = {10.1016/J.MATCHAR.2018.05.053},
url = {https://www.osti.gov/biblio/22805091}, journal = {Materials Characterization},
issn = {1044-5803},
number = ,
volume = 142,
place = {United States},
year = {2018},
month = {8}
}

Works referencing / citing this record:

Automatic projection image registration for nanoscale X-ray tomographic reconstruction
journal, October 2018


Tomographic reconstruction with a generative adversarial network
journal, February 2020


SWIRL: High-performance many-core CPU code generation for deep neural networks
journal, May 2019