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TRIC: Trust Region for Invariant Compactness and Its Application to Abdominal Aorta Segmentation

  • Ismail Ben Ayed
  • Michael Wang
  • Brandon Miles
  • Gregory J. Garvin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

This study investigates segmentation with a novel invariant compactness constraint. The proposed prior is a high-order fractional term, which is not directly amenable to powerful optimizers. We derive first-order Gateâux derivative approximations of our compactness term and adopt an iterative trust region paradigm by splitting our problem into constrained sub-problems, each solving the approximation globally via a Lagrangian formulation and a graph cut. We apply our algorithm to the challenging task of abdominal aorta segmentation in 3D MRI volumes, and report quantitative evaluations over 30 subjects, which demonstrate that the results correlate well with independent manual segmentations. We further show the use of our method in several other medical applications and demonstrate that, in comparison to a standard level-set optimization, our algorithm is one order of magnitude faster.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ismail Ben Ayed
    • 1
  • Michael Wang
    • 2
  • Brandon Miles
    • 3
  • Gregory J. Garvin
    • 4
  1. 1.GE HealthcareLondonCanada
  2. 2.McGill UniversityMontrealCanada
  3. 3.Simon Fraser UniversityBurnabyCanada
  4. 4.St. Joseph’s Health Care HospitalLondonCanada

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