Psoas Major Muscle Segmentation Using Higher-Order Shape Prior

  • Tsutomu Inoue
  • Yoshiro Kitamura
  • Yuanzhong Li
  • Wataru Ito
  • Hiroshi Ishikawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9601)

Abstract

We propose a novel segmentation method based on higher-order graph cuts which enables the utilization of prior knowledge regarding anatomical shapes. We applied the method for segmentation of psoas major muscles by using combinations of logistic curves which representing their shapes. The higher-order terms consisting of variables (voxels) just inside or outside of the estimated shapes are added to the energy function to encourage the segmentation results to fit to the shapes. We verified the effectiveness of the method with 20 abdominal CT images. By comparing the segmentation results to the ground truth data prepared by a clinical expert, we validated the method where it achieved the Jaccard similarity coefficient (JSC) of 75.4 % (right major) and 77.5 % (left major). We also confirmed that the proposed method worked well for thick CT images.

Keywords

Psoas major muscle Abdominal CT images Graph cuts Higher-order potential 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Tsutomu Inoue
    • 1
  • Yoshiro Kitamura
    • 1
    • 2
  • Yuanzhong Li
    • 1
  • Wataru Ito
    • 1
  • Hiroshi Ishikawa
    • 2
    • 3
  1. 1.Imaging Technology CenterFujifilm CorporationTokyoJapan
  2. 2.Department of Computer Science and EngineeringWaseda UniversityTokyoJapan
  3. 3.JST CRESTTokyoJapan

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