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VISCERAL: Towards Large Data in Medical Imaging — Challenges and Directions

  • Georg Langs
  • Allan Hanbury
  • Bjoern Menze
  • Henning Müller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7723)

Abstract

The increasing amount of medical imaging data acquired in clinical practice holds a tremendous body of diagnostically relevant information. Only a small portion of these data are accessible during clinical routine or research due to the complexity, richness, high dimensionality and size of the data. There is consensus in the community that leaps in this regard are hampered by the lack of large bodies of data shared across research groups and an associated definition of joint challenges on which development can focus. In this paper we describe the objectives of the project Visceral. It will provide the means to jump–start this process by providing access to unprecedented amounts of real world imaging data annotated through experts and by using a community effort to generate a large corpus of automatically generated standard annotations. To this end, Visceral will conduct two competitions that tackle large scale medical image data analysis in the fields of anatomy detection, and content–based image retrieval, in this case the retrieval of similar medical cases using visual data and textual radiology reports.

Keywords

Medical imaging Large scale data Localization Retrieval 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Georg Langs
    • 1
  • Allan Hanbury
    • 2
  • Bjoern Menze
    • 3
  • Henning Müller
    • 4
  1. 1.Computational Image Analysis and Radiology Lab Department of RadiologyMedical University of ViennaAustria
  2. 2.Institute for Software Technology and Interactive Systems Faculty of Computer ScienceVienna University of TechnologyAustria
  3. 3.Computer Vision LabETH ZurichSwitzerland
  4. 4.University of Applied Sciences Western Switzerland, Sierre (HES-SO)Switzerland

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