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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

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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