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 · 27 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:: US Army Research Office (ARO); USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1475563

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

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Semantic segmentation of synchrotron tomography of multiphase Al-Si alloys using a convolutional neural network with a pixel-wise weighted loss function Strohmann, Tobias; Bugelnig, Katrin; Breitbarth, Eric Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-56008-7	journal	December 2019
Automatic projection image registration for nanoscale X-ray tomographic reconstruction Yu, Haiyan; Xia, Sihao; Wei, Chenxi Journal of Synchrotron Radiation, Vol. 25, Issue 6 https://doi.org/10.1107/s1600577518013929	journal	October 2018
Tomographic reconstruction with a generative adversarial network Yang, Xiaogang; Kahnt, Maik; Brückner, Dennis Journal of Synchrotron Radiation, Vol. 27, Issue 2 https://doi.org/10.1107/s1600577520000831	journal	February 2020
SWIRL: High-performance many-core CPU code generation for deep neural networks Venkat, Anand; Rusira, Tharindu; Barik, Raj The International Journal of High Performance Computing Applications, Vol. 33, Issue 6 https://doi.org/10.1177/1094342019866247	journal	May 2019
Semantic segmentation of synchrotron tomography of multiphase Al-Si alloys using a convolutional neural network with a pixel-wise weighted loss function Strohmann, Tobias; Bugelnig, Katrin; Breitbarth, Eric RWTH Aachen University https://doi.org/10.18154/rwth-2020-01955	text	January 2019
Tomographic reconstruction with a generative adversarial network Yang, Xiaogang; Kahnt, Maik; Brückner, Dennis FID GEO https://doi.org/10.23689/fidgeo-5047	text	January 2020
Tomographic reconstruction with a generative adversarial network Yang, Xiaogang; Kahnt, Maik; Brückner, Dennis Deutsches Elektronen-Synchrotron, DESY, Hamburg https://doi.org/10.3204/pubdb-2020-01013	text	January 2020
Tomographic reconstruction with a generative adversarial network Yang, Xiaogang; Kahnt, Maik; Brückner, Dennis Karlsruhe https://doi.org/10.5445/ir/1000115487	text	January 2020

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