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U-Net – Deep Learning for Cell Counting, Detection, and Morphometry

Thorsten Falk, Dominic Mai, Robert Bensch, Özgün Çiçek, Ahmed Abdulkadir, Yassine Marrakchi, Anton Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, Thomas Brox, Olaf Ronneberger
Nature Methods, 16: 67--70, Jan 2019
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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BibTex reference

@Article{FMBCAMBBR19,
  author       = "T. Falk and D. Mai and R. Bensch and {\"O}. {\c{C}}i{\c{c}}ek and A. Abdulkadir and Y. Marrakchi and A. B{\"o}hm and J. Deubner and Z. J{\"a}ckel and K. Seiwald and A. Dovzhenko and O. Tietz and C. Dal Bosco and S. Walsh and D. Saltukoglu and T. L. Tay and M. Prinz and K. Palme and M. Simons and I. Diester and T. Brox and O. Ronneberger",
  title        = "U-Net – Deep Learning for Cell Counting, Detection, and Morphometry",
  journal      = "Nature Methods",
  volume       = "16",
  pages        = "67--70",
  month        = "Jan",
  year         = "2019",
  url          = "http://lmb.informatik.uni-freiburg.de/Publications/2019/FMBCAMBBR19"
}

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