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  4. Opening up the "black box" of medical image segmentation with statistical shape models
 
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2013
Journal Article
Title

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

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.
Author(s)
Landesberger, Tatiana von
TU Darmstadt GRIS
Andrienko, Gennady
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Andrienko, Natalia
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Bremm, Sebastian
TU Darmstadt GRIS
Kirschner, Matthias
TU Darmstadt GRIS
Wesarg, Stefan  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kuijper, Arjan  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Journal
The Visual Computer  
DOI
10.1007/s00371-013-0852-y
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • medical imaging

  • medical modeling

  • Visual analytics

  • image segmentation

  • statistical shape models (SSM)

  • spatio-temporal data

  • Forschungsgruppe Visual Search and Analysis (VISA)

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