Segmentation of Diseased Livers: A 3D Refinement Approach

  • R. BeichelEmail author
  • C. Bauer
  • A. Bornik
  • E. Sorantin
  • H. Bischof


Liver segmentation is the first data analysis step in computer-aided planning of liver tumor resections. For clinical applicability, the segmentation approach must be able to cope with the high variation in shape and gray-value appearance of the liver. In this article we present a novel segmentation scheme based on a true 3D segmentation refinement concept utilizing a hybrid desktop/virtual reality user interface. The method consists of two main stages. First, an initial segmentation is generated using graph cuts. Second, a segmentation refinement step allows to fix arbitrary segmentation errors. We demonstrate the robustness of our method on ten contrast enhanced liver CT scans and compare it to fifteen other methods. Our segmentation approach copes successfully with the high variation found in patient data sets and allows to produce a segmentation in a time-efficient manner.


Initial Segmentation Segmentation Error Compute Tomography Volume Liver Segmentation Hybrid User Interface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by the Austrian Science Fund (FWF) under Grants P17066-N04 and Y193 and the Doctoral Program Confluence of Vision and Graphics W1209-N15


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • R. Beichel
    • 1
    Email author
  • C. Bauer
    • 2
  • A. Bornik
    • 3
  • E. Sorantin
    • 4
  • H. Bischof
    • 3
  1. 1.Department of Electrical and Computer Engineering and Department of Internal MedicineThe University of IowaIowa CityUSA
  2. 2.Department of Electrical and Computer EngineeringThe University of IowaIowa CityUSA
  3. 3.Institute for Computer Graphics and VisionGraz University of TechnologyGrazAustria
  4. 4.Research Unit for Digital Information and Image Processing, Department of RadiologyMedical University GrazGrazAustria

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