Liver and tumor segmentation and analysis from CT of diseased patients via a generic affine invariant shape parameterization and graph cuts

  • Authors:

  • Marius George Linguraru;William J. Richbourg;Jeremy M. Watt;Vivek Pamulapati;Ronald M. Summers

  • Affiliations:

  • Imaging Biomarkers and Computer Aided Laboratory, Radiology and Imaging Sciences, National Institutes of Health , Clinical Center, Bethesda, MD;Imaging Biomarkers and Computer Aided Laboratory, Radiology and Imaging Sciences, National Institutes of Health , Clinical Center, Bethesda, MD;Imaging Biomarkers and Computer Aided Laboratory, Radiology and Imaging Sciences, National Institutes of Health , Clinical Center, Bethesda, MD;Imaging Biomarkers and Computer Aided Laboratory, Radiology and Imaging Sciences, National Institutes of Health , Clinical Center, Bethesda, MD;Imaging Biomarkers and Computer Aided Laboratory, Radiology and Imaging Sciences, National Institutes of Health , Clinical Center, Bethesda, MD

  • Venue:
  • MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
  • Year:
  • 2011

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Abstract

The paper presents the automated segmentation of livers from abdominal CT images of diseased populations from images with inconsistent enhancement. A novel three-dimensional (3D) affine invariant shape parameterization is employed to compare local shape across organs. By generating a regular sampling of the organ's surface, this parameterization can be effectively used to compare features of a set of closed 3D surfaces point-to-point while avoiding common problems with the parameterization of concave surfaces. From an initial segmentation, the areas of atypical local shape are determined using training sets. A geodesic active contour corrects locally the segmentations of organs in abnormal images and optimized graph cuts segment the vasculature and hepatic tumors using shape and enhancement constraints. Liver segmentation errors are reduced significantly, all tumors are detected and the tumor burden is estimated with 0.9% error. Results from test data demonstrate the method's robustness to analyze livers from difficult clinical cases to allow temporal monitoring of patients with hepatic cancer.