Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-26T22:37:12.588Z Has data issue: false hasContentIssue false
  • English
  • Français

Iterative Machine Learning Method for Pore-Back Artifact Mitigation in High Porosity Membrane FIB-SEM Image Segmentation

Published online by Cambridge University Press:  05 August 2019

Joseph Tracey ,
Sam Sheng Lin ,
Jasna Jankovic ,
Aiden Zhu  and
Shawn Zhang
Joseph Tracey
Affiliation:
Materials Science and Engineering Department, University of Connecticut, Storrs, Connecticut, USA.
Sam Sheng Lin
Affiliation:
DigiM Solution LLC, Burlington, Massachusetts, USA.
Jasna Jankovic
Affiliation:
Materials Science and Engineering Department, University of Connecticut, Storrs, Connecticut, USA.
Aiden Zhu
Affiliation:
DigiM Solution LLC, Burlington, Massachusetts, USA.
Shawn Zhang
Affiliation:
DigiM Solution LLC, Burlington, Massachusetts, USA.

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Data Acquisition Schemes, Machine Learning Algorithms, and Open Source Software Development for Electron Microscopy
Information
Microscopy and Microanalysis , Volume 25 , Supplement S2 , August 2019 , pp. 186 - 187
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Zhang, S. et al. , Microscopy Today 27 (2019). doi:10.1017/S1551929519000026Google Scholar
[2]Dravid, A, Microscopy Today 27(1) (2019), p. 18.Google Scholar