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

C. Shashank Kaira, Xiaogang Yang, Vincent De Andrade, Francesco De Carlo, William Scullin, Doga Gursoy, Nikhilesh Chawla

Research output: Contribution to journalArticle

10 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)203-210
Number of pages8
JournalMaterials Characterization
Volume142
DOIs
StatePublished - Aug 1 2018

Keywords

  • Aluminum alloys
  • Deep learning
  • Precipitates
  • Segmentation
  • Transmission X-ray Microscopy (TXM)

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

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