Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets

Edouard A. Hay; Raghuveer Parthasarathy

doi:10.1371/journal.pcbi.1006628

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Open Access

Peer-reviewed

Research Article

Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets

Edouard A. Hay,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Writing – original draft, Writing – review & editing

Affiliation Department of Physics, Institute of Molecular Biology, Materials Science Institute, University of Oregon, Eugene, Oregon, United States of America
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Raghuveer Parthasarathy

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – review & editing

* E-mail: raghu@uoregon.edu

Affiliation Department of Physics, Institute of Molecular Biology, Materials Science Institute, University of Oregon, Eugene, Oregon, United States of America

http://orcid.org/0000-0002-6006-4749

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Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets

Edouard A. Hay,
Raghuveer Parthasarathy

Published: December 3, 2018
https://doi.org/10.1371/journal.pcbi.1006628
See the preprint

About the Authors

Edouard A. Hay

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Writing – original draft, Writing – review & editing

Affiliation Department of Physics, Institute of Molecular Biology, Materials Science Institute, University of Oregon, Eugene, Oregon, United States of America

Raghuveer Parthasarathy

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – review & editing

* E-mail: raghu@uoregon.edu

Affiliation Department of Physics, Institute of Molecular Biology, Materials Science Institute, University of Oregon, Eugene, Oregon, United States of America

http://orcid.org/0000-0002-6006-4749

Competing Interests

The authors have declared that no competing interests exist.

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Neural networks
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Zebrafish
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Gut bacteria
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Imaging techniques
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Machine learning
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Vibrio
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Gastrointestinal tract
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Larvae
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