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Automatic 3D Reconstruction by Deep Learning Neural Networks Using Images Acquired via 4D-STEM Stereo Imaging

Published online by Cambridge University Press:  22 July 2022

Gulnaz Ganeeva ,
Okan Altingövde ,
Quy Ong Khac ,
Francesco Stellacci ,
Pascal Fua ,
Emad Oveisi  and
Cécile Hébert
Gulnaz Ganeeva*
Affiliation:
Electron Spectrometry and Microscopy Laboratory (LSME), EPFL, Lausanne, Switzerland
Okan Altingövde
Affiliation:
Computer Vision Laboratory (CVLab), EPFL, Lausanne, Switzerland
Quy Ong Khac
Affiliation:
Supramolecular Nanomaterials and Interfaces Laboratory (SuNMIL), EPFL, Lausanne, Switzerland
Francesco Stellacci
Affiliation:
Supramolecular Nanomaterials and Interfaces Laboratory (SuNMIL), EPFL, Lausanne, Switzerland
Pascal Fua
Affiliation:
Computer Vision Laboratory (CVLab), EPFL, Lausanne, Switzerland
Emad Oveisi*
Affiliation:
Interdisciplinary Centre for Electron Microscopy (CIME), EPFL, Lausanne, Switzerland
Cécile Hébert*
Affiliation:
Electron Spectrometry and Microscopy Laboratory (LSME), EPFL, Lausanne, Switzerland
*
*Corresponding authors: [email protected], [email protected], [email protected]
*Corresponding authors: [email protected], [email protected], [email protected]
*Corresponding authors: [email protected], [email protected], [email protected]

Abstract

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Type
Advanced 3D Imaging and Analysis Methods for New Opportunities in Material Science
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 218 - 220
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Copyright
Copyright © Microscopy Society of America 2022

References

Jácome, A et al. , ‘Three-Dimensional Reconstruction and Quantification of Dislocation Substructures from Transmission Electron Microscopy Stereo Pairs’, Ultramicroscopy, 195 (2018), 157-170, doi: 10.1016/j.ultramic.2018.08.015.CrossRefGoogle Scholar
Oveisi, E, Letouzey, A, De Zanet, S et al. , ‘Stereo-Vision Three-Dimensional Reconstruction of Curvilinear Structures Imaged with a TEM’, Ultramicroscopy, 184 (2018), 116–24, doi: 10.1016/j.ultramic.2017.08.010.CrossRefGoogle Scholar
Oveisi, E, Letouzey, A, Alexander, D T L, et al. , ‘Tilt-Less 3-D Electron Imaging and Reconstruction of Complex Curvilinear Structures’, Scientific Reports, 7.1 (2017), doi: 10.1038/s41598-017-07537-6.CrossRefGoogle ScholarPubMed
Zhu, Y et al. , ‘Towards Bend-Contour-Free Dislocation Imaging via Diffraction Contrast STEM’, Ultramicroscopy, 193. December 2017 (2018), 12–23, doi: 10.1016/j.ultramic.2018.06.001.CrossRefGoogle Scholar
Altingövde, O et al. , ‘3D reconstruction of curvilinear structures with stereo matching deep convolutional neural networks’, Ultramicroscopy, 234 (2022), doi: 10.1016/j.ultramic.2021.113460.CrossRefGoogle ScholarPubMed
Ophus, C, ‘Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM): From Scanning Nanodiffraction to Ptychography and Beyond’, Microsc Microanal. (2019), 563-582. doi: 10.1017/S1431927619000497.CrossRefGoogle ScholarPubMed