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Liver Workbench: A Tool Suite for Liver and Liver Tumor Segmentation and Modeling

  • Jiayin Zhou
  • Wei Xiong
  • Feng Ding
  • Weimin Huang
  • Tian Qi
  • Zhimin Wang
  • Thiha Oo
  • Sudhakar Kundapur Venkatesh
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 120)

Abstract

Robust and efficient liver and tumor segmentation segmentation tools from CT images are important for clinical decision-making in liver treatment planning and response evaluation. In this work, we report recent advances in an ongoing project Liver Workbench which aims to provide a suite of tools for the segmentation segmentation, quantification and modeling of various objects in CT images such as the liver, its vessels and tumors. Firstly, a liver segmentation segmentation approach is described. It registers a liver mesh model model to actual image features by adopting noise-insensitive flipping-free mesh deformations. Next, a propagation learning approach is incorporated into a semi-automatic classification method for robust segmentation segmentation of liver tumors based on liver ROI obtained. Finally, an unbiased probabilistic liver atlas construction technique is adopted to embody the shape and intensity variation to constrain liver segmentation segmentation. We also report preliminary experimental results.

Keywords

Gaussian Mixture Model Selective Internal Radiation Therapy Mesh Deformation Mesh Vertex Tumor Segmentation 
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.

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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Jiayin Zhou
    • 1
  • Wei Xiong
    • 1
  • Feng Ding
    • 2
  • Weimin Huang
    • 1
  • Tian Qi
    • 1
  • Zhimin Wang
    • 1
  • Thiha Oo
    • 1
  • Sudhakar Kundapur Venkatesh
    • 3
  1. 1.Institute for Infocomm ResearchA*STARSingaporeSingapore
  2. 2.Department of Diagnostic Radiology, School of ComputingNational University of SingaporeSingaporeSingapore
  3. 3.Department of Diagnostic RadiologyNational University of SingaporeSingaporeSingapore

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