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

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.
Authors:
 [1] ;  [2] ;  [2] ;  [2] ;  [3] ;  [2] ;  [1]
  1. Arizona State Univ., Tempe, AZ (United States). Center for 4D Materials Science
  2. Argonne National Lab. (ANL), Argonne, IL (United States). Advanced Photon Source (APS)
  3. Argonne National Lab. (ANL), Argonne, IL (United States). Argonne Leadership Computing Facility
Publication Date:
Grant/Contract Number:
AC02-06CH11357; W911NF1410550
Type:
Accepted Manuscript
Journal Name:
Materials Characterization
Additional Journal Information:
Journal Volume: 142; Journal Issue: C; Journal ID: ISSN 1044-5803
Publisher:
Elsevier
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); US Army Research Office (ARO)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Aluminum alloys; Deep learning; Precipitates; Segmentation; Transmission X-ray Microscopy (TXM)
OSTI Identifier:
1475563

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., 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. doi: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. 2018. "Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning". United States. doi:10.1016/j.matchar.2018.05.053.
@article{osti_1475563,
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 = {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},
journal = {Materials Characterization},
number = C,
volume = 142,
place = {United States},
year = {2018},
month = {5}
}