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AtomSegNet and TomoFillNet—Two Deep Learning Open-Source Apps for Superresolution Processing of Atomic Resolution Images and Missing-wedge Information Inpainting in Electron Tomograms

Published online by Cambridge University Press:  30 July 2020

Huolin Xin ,
Ruoqian Lin ,
Rui Zhang ,
Chunyang Wang ,
Guanglei Ding  and
He Wei
Huolin Xin
Affiliation:
University of California-Irvine, Irvine, California, United States
Ruoqian Lin
Affiliation:
Brookhaven National Laboratory, Upton, New York, United States
Rui Zhang
Affiliation:
University of California-Irvine, Irvine, California, United States
Chunyang Wang
Affiliation:
University of California-Irvine, Irvine, California, United States
Guanglei Ding
Affiliation:
University of California-Irvine, Irvine, California, United States
He Wei
Affiliation:
University of California-Irvine, Irvine, California, United States

Abstract

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Type
Advances in Modeling, Simulation, and Artificial Intelligence in Microscopy and Microanalysis for Physical and Biological Systems
Information
Microscopy and Microanalysis , Volume 26 , Supplement S2 , August 2020 , pp. 926
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Copyright
Copyright © Microscopy Society of America 2020

References

Ding, G., Liu, Y., Zhang, R. & Xin, H. L. A joint deep learning model to recover information and reduce artifacts in missing-wedge sinograms for electron tomography and beyond. Scientific reports 9, 12803, doi:10.1038/s41598-019-49267-x (2019).CrossRefGoogle ScholarPubMed