Vertebral body segmentation in MRI via convex relaxation and distribution matching
Vertebral body segmentation in MRI via convex relaxation and distribution matching
Ben Ayed, Ismail and Punithakumar, Kumaradevan and Minhas, Rashid and Joshi, Rohit and Garvin, Gregory J.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012
Abstract : We state vertebral body (VB) segmentation in MRI as a distribution-matching problem, and propose a convex-relaxation solution which is amenable to parallel computations. The proposed algorithm does not require a complex learning from a large manually-built training set, as is the case of the existing methods. From a very simple user input, which amounts to only three points for a whole volume, we compute a multi-dimensional model distribution of features that encode contextual information about the VBs. Then, we optimize a functional containing (1) a feature-based constraint which evaluates a similarity between distributions, and (2) a total-variation constraint which favors smooth surfaces. Our formulation leads to a challenging problem which is not directly amenable to convex-optimization techniques. To obtain a solution efficiently, we split the problem into a sequence of sub-problems, each can be solved exactly and globally via a convex relaxation and the augmented Lagrangian method. Our parallelized implementation on a graphics processing unit (GPU) demonstrates that the proposed solution can bring a substantial speed-up of more than 30 times for a typical 3D spine MRI volume. We report quantitative performance evaluations over 15 subjects, and demonstrate that the results correlate well with independent manual segmentations.
