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Automated Containerized Medical Image Processing Based on MITK and Python

A Modular System to Implement Medical Image Processing Pipelines and Visualize Meta Data
  • Caspar J. Goch
  • Jasmin Metzger
  • Martin Hettich
  • André Klein
  • Tobias Norajitra
  • Michael Götz
  • Jens Petersen
  • Klaus H. Maier-Hein
  • Marco Nolden
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Modern medical image processing employs an ever widening array of tools on an ever larger pool of data. Development of the tools takes place on a variety of different platforms, determined by external circumstances, such as the availability of necessary functionality as libraries forcing the use of a specific programming language or interdependencies of third party tools requiring specific versions of specific operating systems. This problem itself is not unique to medical imaging[1] and solutions such as Docker have proven themselves useful in a variety of use cases.

Literatur

  1. 1.
    Gil Y, Deelman E, Ellisman M, et al. Examining the challenges of scientific workflows. Computer;40(12):24–32.Google Scholar
  2. 2.
    Nolden M, Zelzer S, Seitel A, et al. The medical imaging interaction toolkit: challenges and advances. International Journal of Computer Assisted Radiology and Surgery. 2013;8(4):607–620.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Caspar J. Goch
    • 1
  • Jasmin Metzger
    • 1
  • Martin Hettich
    • 1
  • André Klein
    • 1
  • Tobias Norajitra
    • 1
  • Michael Götz
    • 1
  • Jens Petersen
    • 1
  • Klaus H. Maier-Hein
    • 1
  • Marco Nolden
    • 1
  1. 1.Medical Image Computing GroupGerman Cancer Research Center (DKFZ)HeidelbergDeutschland

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