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The Visual Computer

, Volume 29, Issue 9, pp 893–905 | Cite as

Opening up the “black box” of medical image segmentation with statistical shape models

  • Tatiana von LandesbergerEmail author
  • Gennady Andrienko
  • Natalia Andrienko
  • Sebastian Bremm
  • Matthias Kirschner
  • Stefan Wesarg
  • Arjan Kuijper
Original Article

Abstract

The importance of medical image segmentation increases in fields like treatment planning or computer aided diagnosis. For high quality automatic segmentations, algorithms based on statistical shape models (SSMs) are often used. They segment the image in an iterative way. However, segmentation experts and other users can only asses the final segmentation results, as the segmentation is performed in a “black box manner”. Users cannot get deeper knowledge on how the (possibly bad) output was produced. Moreover, they do not see whether the final output is the result of a stabilized process.

We present a novel Visual Analytics method, which offers this desired deeper insight into the image segmentation. Our approach combines interactive visualization and automatic data analysis. It allows the expert to assess the quality development (convergence) of the model both on global (full organ) and local (organ areas, landmarks) level. Thereby, local patterns in time and space, e.g., non-converging parts of the organ during the segmentation, can be identified. The localization and specifications of such problems helps the experts creating segmentation algorithms to identify algorithm drawbacks and thus it may point out possible ways how to improve the algorithms systematically.

We apply our approach on real-world data showing its usefulness for the analysis of the segmentation process with statistical shape models.

Keywords

Medical imaging Medical modeling Visual analytics Image segmentation Statistical shape models Spatio-temporal data 

Notes

Acknowledgements

The work has been partially supported by the DFG SPP 1335 project “Visual Analytics Methods for Modeling in Medical Imaging”. The authors would like to thank J. Beutel for his support with the project.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tatiana von Landesberger
    • 1
    Email author
  • Gennady Andrienko
    • 2
  • Natalia Andrienko
    • 2
  • Sebastian Bremm
    • 1
  • Matthias Kirschner
    • 1
  • Stefan Wesarg
    • 3
  • Arjan Kuijper
    • 3
  1. 1.TU DarmstadtDarmstadtGermany
  2. 2.Fraunhofer IAISBonnGermany
  3. 3.Fraunhofer IGDDarmstadtGermany

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