title: TRIC: trust region for invariant compactness and its application to abdominal aorta segmentation.
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.
authors.forename: Ismail
authors.initials: I
authors.lastname: Ben Ayed
authors.forename: Michael
authors.initials: M
authors.lastname: Wang
authors.forename: Brandon
authors.initials: B
authors.lastname: Miles
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Garvin
mesh_terms: D000465:Algorithms; D001012:Aorta, Abdominal / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity