U-Net: deep learning for cell counting, detection, and morphometry
U-Net: deep learning for cell counting, detection, and morphometry
About this item
Full title
Author / Creator
Falk, Thorsten , Mai, Dominic , Bensch, Robert , Çiçek, Özgün , Abdulkadir, Ahmed , Marrakchi, Yassine , Böhm, Anton , Deubner, Jan , Jäckel, Zoe , Seiwald, Katharina , Dovzhenko, Alexander , Tietz, Olaf , Dal Bosco, Cristina , Walsh, Sean , Saltukoglu, Deniz , Tay, Tuan Leng , Prinz, Marco , Palme, Klaus , Simons, Matias , Diester, Ilka , Brox, Thomas and Ronneberger, Olaf
Publisher
New York: Nature Publishing Group US
Journal title
Language
English
Formats
Publication information
Publisher
New York: Nature Publishing Group US
Subjects
More information
Scope and Contents
Contents
U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pret...
Alternative Titles
Full title
U-Net: deep learning for cell counting, detection, and morphometry
Authors, Artists and Contributors
Author / Creator
Mai, Dominic
Bensch, Robert
Çiçek, Özgün
Abdulkadir, Ahmed
Marrakchi, Yassine
Böhm, Anton
Deubner, Jan
Jäckel, Zoe
Seiwald, Katharina
Dovzhenko, Alexander
Tietz, Olaf
Dal Bosco, Cristina
Walsh, Sean
Saltukoglu, Deniz
Tay, Tuan Leng
Prinz, Marco
Palme, Klaus
Simons, Matias
Diester, Ilka
Brox, Thomas
Ronneberger, Olaf
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_miscellaneous_2158248176
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2158248176
Other Identifiers
ISSN
1548-7091
E-ISSN
1548-7105
DOI
10.1038/s41592-018-0261-2
