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Enforcing Prediction Consistency Across Orthogonal Planes Significantly Improves Segmentation of FIB-SEM Image Volumes by 2D Neural Networks.

Published online by Cambridge University Press:  30 July 2020

Ryan Conrad ,
Hanbin Lee  and
Kedar Narayan
Ryan Conrad
Affiliation:
National Cancer Institute, NIH & Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States
Hanbin Lee
Affiliation:
National Cancer Institute, NIH & Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States
Kedar Narayan
Affiliation:
National Cancer Institute, NIH & Frederick National Laboratory for Cancer Research, Frederick, Maryland, 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. 2128 - 2130
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Copyright
Copyright © Microscopy Society of America 2020

References

Xiao, C., Chen, X., Li, W., Li, L., Wang, L., Xie, Q., & Han, H. (2018). Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network. Frontiers in Neuroanatomy 12, 92.10.3389/fnana.2018.00092CrossRefGoogle Scholar
Reed, S. E., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., & Rabinovich, A. (2014). Training Deep Learning Neural Networks on Noisy Labels with Bootstrapping. https://arxiv.org/abs/1412.6596Google Scholar
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016, 565–571. http://arxiv.org/abs/1606.04797 10.1109/3DV.2016.79CrossRefGoogle Scholar
Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. Lecture Notes in Computer Science, 11211 LNCS, 833–851.10.1007/978-3-030-01234-2_49CrossRefGoogle Scholar
Smith, L. N. (2018). A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay. http://arxiv.org/abs/1803.09820Google Scholar
We thank Adam Harned for acquiring the FIB-SEM datasets used throughout this work and Dr. Stanley Lipkowitz for providing the cell samples. This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. 75N91019D00024. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.Google Scholar