Automatic image annotation for fluorescent cell nuclei segmentation

Fabian Englbrecht; Iris E. Ruider; Andreas R. Bausch

doi:10.1371/journal.pone.0250093

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Open Access

Peer-reviewed

Research Article

Automatic image annotation for fluorescent cell nuclei segmentation

Fabian Englbrecht ,

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

* E-mail: fabian.englbrecht@tum.de

Affiliation Lehrstuhl für Biophysik (E27), Technische Universität München (TUM), Garching, Germany

https://orcid.org/0000-0001-7743-4593

⨯
Iris E. Ruider,

Roles Data curation, Visualization, Writing – original draft

Affiliation Lehrstuhl für Biophysik (E27), Technische Universität München (TUM), Garching, Germany
⨯
Andreas R. Bausch

Roles Conceptualization, Supervision

Affiliations Lehrstuhl für Biophysik (E27), Technische Universität München (TUM), Garching, Germany, Center for Protein Assemblies (CPA), Garching, Germany
⨯

Automatic image annotation for fluorescent cell nuclei segmentation

Fabian Englbrecht,
Iris E. Ruider,
Andreas R. Bausch

Published: April 16, 2021
https://doi.org/10.1371/journal.pone.0250093

About the Authors

Fabian Englbrecht

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

* E-mail: fabian.englbrecht@tum.de

Affiliation Lehrstuhl für Biophysik (E27), Technische Universität München (TUM), Garching, Germany

https://orcid.org/0000-0001-7743-4593

Iris E. Ruider

Roles Data curation, Visualization, Writing – original draft

Affiliation Lehrstuhl für Biophysik (E27), Technische Universität München (TUM), Garching, Germany

Andreas R. Bausch

Roles Conceptualization, Supervision

Affiliations Lehrstuhl für Biophysik (E27), Technische Universität München (TUM), Garching, Germany, Center for Protein Assemblies (CPA), Garching, Germany

Competing Interests

The authors have declared that no competing interests exist.

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Neural networks
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Fluorescence imaging
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Deep learning
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In vivo imaging
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Memory recall
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Supervised machine learning
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Imaging techniques
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Red nucleus
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