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  4. Reducing over- and Undersegmentations of the liver in computed tomographies using anatomical knowledge
 
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2016
Conference Paper
Titel

Reducing over- and Undersegmentations of the liver in computed tomographies using anatomical knowledge

Abstract
In the last decades several liver segmentation methods have been proposed. The proposed methods go from region growing to the more complex statistical shape models. Despite the robustness of those algorithms, liver segmentation is still a challenging task especially in areas in which its neighboring organs have similar intensities, e.g., heart and ribcage. In addition to this, pathological organs that contain tumors near their surface present additional difficulties. This paper presents a solution to increase the accuracy of those algorithms in the aforementioned areas. The effect of the improvement using the generated heart and ribcage walls (7% and 1% respectively) is evaluated on 9 clinical computer tomographies (CT). The improvement (12 %) when tumors are near the surface, on the contrary, is tested on 7 clinical CT images.
Author(s)
Oyarzun Laura, Cristina
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Oelmann, Simon
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Drechsler, Klaus
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Wesarg, Stefan
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Hauptwerk
XIV Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016
Konferenz
Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON) 2016
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DOI
10.1007/978-3-319-32703-7_75
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • liver

  • segmentation

  • Lead Topic: Individual Health

  • Research Line: Computer vision (CV)

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