Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-25T08:02:14.400Z Has data issue: false hasContentIssue false
  • English
  • Français

Problems and Progress in Automating Electron Microscopy Segmentation

Published online by Cambridge University Press:  01 August 2018

Matthew Guay ,
Zeyad Emam  and
Richard Leapman
Matthew Guay
Affiliation:
National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
Zeyad Emam
Affiliation:
AMSC Department, University of Maryland, College Park, MD, USA
Richard Leapman
Affiliation:
National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, 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
Abstract
Information
Microscopy and Microanalysis , Volume 24 , Supplement S1: Proceedings of Microscopy & Microanalysis 2018 , August 2018 , pp. 508 - 509
Copyright
© Microscopy Society of America 2018 

References

[1] Denk, Winfried Horstmann, Heinz Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS biology 2.11 2004 e329.Google Scholar
[2] Ronneberger, Olaf, Philipp, Fischer Brox, Thomas “U-net: Convolutional networks for biomedical image segmentation.” International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.Google Scholar
[3] Qicek, Ozgun, et al “3D U-Net: learning dense volumetric segmentation from sparse annotation.” International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2016.Google Scholar
[4] Guay, Matthew D., et al Neural Network Ensembles Will Enable Teravoxel Image Segmentation for Electron Microscopy. Biophysical Journal 114.3 2018 343a.Google Scholar