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. 2019 Jan;16(1):67-70.
doi: 10.1038/s41592-018-0261-2. Epub 2018 Dec 17.

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

Thorsten Falk #  1   2   3 , Dominic Mai #  1   2   4   5 , Robert Bensch #  1   2   6 , Özgün Çiçek  1 , Ahmed Abdulkadir  1   7 , Yassine Marrakchi  1   2   3 , Anton Böhm  1 , Jan Deubner  8   9 , Zoe Jäckel  8   9 , Katharina Seiwald  8 , Alexander Dovzhenko  10   11 , Olaf Tietz  10   11 , Cristina Dal Bosco  10 , Sean Walsh  10   11 , Deniz Saltukoglu  2   12   13   14 , Tuan Leng Tay  9   15   16 , Marco Prinz  2   3   15 , Klaus Palme  2   10 , Matias Simons  2   12   13   17 , Ilka Diester  8   9   18 , Thomas Brox  1   2   3   9 , Olaf Ronneberger  19   20   21
Affiliations

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

Thorsten Falk et al. Nat Methods. 2019 Jan.

Erratum in

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.

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References

    1. Sommer, C, Strähle, C, Koethe, U. & Hamprecht, F. A. in Ilastik: interactive learning and segmentation toolkit in IEEE Int. Symp. Biomed. Imaging. 230–233 (IEEE: Piscataway, NJ, USA, 2011).
    1. Arganda-Carreras, I. et al. Bioinformatics 33, 2424–2426 (2017). - DOI
    1. Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015 Vol. 9351, 234–241 (Springer, Cham, Switzerland, 2015).
    1. Rusk, N. Nat. Methods 13, 35 (2016). - DOI
    1. Webb, S. Nature 554, 555–557 (2018). - DOI

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