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

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

doi:10.1016/j.matchar.2018.05.053

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

Journal Article · 28 May 2018 · Materials Characterization

DOI: https://doi.org/10.1016/j.matchar.2018.05.053 · OSTI ID:1475563

Shashank Kaira, C. ^[1]; Yang, Xiaogang ^[2]; De Andrade, Vincent ^[2]; De Carlo, Francesco ^[2]; Scullin, William ^[3]; Gursoy, Doga ^[2]; Chawla, Nikhilesh ^[1]

Arizona State Univ., Tempe, AZ (United States). Center for 4D Materials Science
Argonne National Lab. (ANL), Argonne, IL (United States). Advanced Photon Source (APS)
Argonne National Lab. (ANL), Argonne, IL (United States). Argonne Leadership Computing Facility

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.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); US Army Research Office (ARO)

Grant/Contract Number:: AC02-06CH11357; W911NF1410550

OSTI ID:: 1475563

Alternate ID(s):: OSTI ID: 1582783

Journal Information:: Materials Characterization, Vol. 142, Issue C; ISSN 1044-5803

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 24 works

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