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

Journal Article · Wed Aug 15 04:00:00 EDT 2018 · Materials Characterization

DOI:https://doi.org/10.1016/J.MATCHAR.2018.05.053· OSTI ID:22805091

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

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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)

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.

• OSTI ID: 22805091
• Journal Information: Materials Characterization, Journal Name: Materials Characterization Vol. 142; ISSN 1044-5803; ISSN MACHEX
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
ALUMINIUM ALLOYS
COMPUTERIZED SIMULATION
COMPUTERIZED TOMOGRAPHY
DATA ANALYSIS
NEURAL NETWORKS
PRECIPITATION
SCANNING ELECTRON MICROSCOPY
X RADIATION