Deep learning kidney segmentation with very limited training data using a cascaded convolution neural network

Junyu Guo; Ayobami Odu; Ivan Pedrosa

doi:10.1371/journal.pone.0267753

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Open Access

Peer-reviewed

Research Article

Deep learning kidney segmentation with very limited training data using a cascaded convolution neural network

Junyu Guo ,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing

* E-mail: junyu.guo@utsouthwestern.edu

Affiliation Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America

https://orcid.org/0000-0002-1173-9666

⨯
Ayobami Odu,

Roles Data curation

Affiliation Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
⨯
Ivan Pedrosa

Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

Affiliation Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
⨯

Deep learning kidney segmentation with very limited training data using a cascaded convolution neural network

Junyu Guo,
Ayobami Odu,
Ivan Pedrosa

Published: May 9, 2022
https://doi.org/10.1371/journal.pone.0267753

About the Authors

Junyu Guo

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing

* E-mail: junyu.guo@utsouthwestern.edu

Affiliation Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America

https://orcid.org/0000-0002-1173-9666

Ayobami Odu

Roles Data curation

Affiliation Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America

Ivan Pedrosa

Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

Affiliation Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America

Competing Interests

The authors have declared that no competing interests exist.

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Magnetic resonance imaging
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Kidneys
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Deep learning
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Fats
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Image processing
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Imaging techniques
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Computed axial tomography
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Neural networks
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