U-Net: deep learning for cell counting, detection, and morphometry View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12-17

AUTHORS

Thorsten Falk, Dominic Mai, Robert Bensch, Özgün Çiçek, Ahmed Abdulkadir, Yassine Marrakchi, Anton Böhm, Jan Deubner, Zoe Jäckel, Katharina Seiwald, Alexander Dovzhenko, Olaf Tietz, Cristina Dal Bosco, Sean Walsh, Deniz Saltukoglu, Tuan Leng Tay, Marco Prinz, Klaus Palme, Matias Simons, Ilka Diester, Thomas Brox, Olaf Ronneberger

ABSTRACT

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 pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples. More... »

PAGES

67-70

References to SciGraph publications

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  • Journal

    TITLE

    Nature Methods

    ISSUE

    1

    VOLUME

    16

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41592-018-0261-2

    DOI

    http://dx.doi.org/10.1038/s41592-018-0261-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1110383802

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30559429


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