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Deep Learning Based Segmentation of Nuclei from Fluorescence Microscopy Images

Published online by Cambridge University Press:  05 August 2019

Prabhakar R. Gudla ,
George Zaki ,
Sigal Shachar ,
Tom Misteli  and
Gianluca Pegoraro
Prabhakar R. Gudla
Affiliation:
High-throughput Imaging Facility (HiTIF), National Institutes of Health, Bethesda, MD, USA. National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
George Zaki
Affiliation:
Biomedical Informatics and Data Science Directorate, Frederick National Lab for Cancer Research, Frederick, MD, USA.
Sigal Shachar
Affiliation:
Cell Biology of Genomes Group, National Institutes of Health, Bethesda, MD, USA. National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Tom Misteli
Affiliation:
Cell Biology of Genomes Group, National Institutes of Health, Bethesda, MD, USA. National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Gianluca Pegoraro*
Affiliation:
High-throughput Imaging Facility (HiTIF), National Institutes of Health, Bethesda, MD, USA. National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
*
*Corresponding author: gianluca.pegoraro@nih.gov

Abstract

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Type
From Images to Insights: Working with Large Data in Cell Biological Imaging
Information
Microscopy and Microanalysis , Volume 25 , Supplement S2 , August 2019 , pp. 1376 - 1377
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Pegoraro, G and Misteli, T, Trends Genet 33 (2017), p. 604.Google Scholar
[2]Falk, T et al. , Nat Methods 16 (2019), p. 67.Google Scholar
[3]He, K et al. , arXiv e-prints [Online], (2015).Google Scholar
[4]Meyer, F, Comp Imag Vis 2 (1994), p. 77.Google Scholar
[5]W Abdulla, [Online], (2017), https://github.com/matterport/Mask_RCNN.Google Scholar
[6]This research was supported by funding from the Center for Cancer Research as part of the Intramural Research Program at NCI/NIH. The authors acknowledge the CBIIT Server Team and the High-Performance Computing Group at NCI/NIH for computational support. Prabhakar R. Gudla and George Zaki contributed equally.Google Scholar