ilastik: interactive machine learning for (bio)image analysis.

Journal: Nature methods
Published Date: Dec 1, 2019

Abstract

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.

Authors

  • Stuart Berg
    HHMI Janelia Research Campus, Ashburn, Virginia, USA.
  • Dominik Kutra
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Thorben Kroeger
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Christoph N Straehle
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Bernhard X Kausler
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Carsten Haubold
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Martin Schiegg
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Janez Ales
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Thorsten Beier
    Division for Computational Genomics & Systems Genetics, German Cancer Research Center, Heidelberg, Germany.
  • Markus Rudy
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Kemal Eren
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Jaime I Cervantes
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Buote Xu
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Fynn Beuttenmueller
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Adrian Wolny
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Chong Zhang
    Department of Big Data Management and Application, School of International Economics and Management, Beijing Technology and Business University, Beijing 100048, China.
  • Ullrich Koethe
    HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Fred A Hamprecht
    Heidelberg University, HCI, Speyerer Str. 6, D-69115 Heidelberg, Germany. Electronic address: fred.hamprecht@uni-heidelberg.de.
  • Anna Kreshuk
    EMBL, Heidelberg, Germany.

Keywords

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