Rapid Multi-organ Segmentation Using Context Integration and Discriminative Models

  • Nathan Lay
  • Neil Birkbeck
  • Jingdan Zhang
  • S. Kevin Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


We propose a novel framework for rapid and accurate segmentation of a cohort of organs. First, it integrates local and global image context through a product rule to simultaneously detect multiple landmarks on the target organs. The global posterior integrates evidence over all volume patches, while the local image context is modeled with a local discriminative classifier. Through non-parametric modeling of the global posterior, it exploits sparsity in the global context for efficient detection. The complete surface of the target organs is then inferred by robust alignment of a shape model to the resulting landmarks and finally deformed using discriminative boundary detectors. Using our approach, we demonstrate efficient detection and accurate segmentation of liver, kidneys, heart, and lungs in challenging low-resolution MR data in less than one second, and of prostate, bladder, rectum, and femoral heads in CT scans, in roughly one to three seconds and in both cases with accuracy fairly close to inter-user variability.


Local & global context context integration multi-landmark detection discriminative learning multi-organ segmentation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nathan Lay
    • 1
  • Neil Birkbeck
    • 1
  • Jingdan Zhang
    • 1
  • S. Kevin Zhou
    • 1
  1. 1.Siemens Corporate TechnologyPrincetonUSA

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