Title: Optimal Multi-Object Segmentation with Novel Gradient Vector Flow Based Shape Priors,
Abstract: Shape priors have been widely utilized in medical image segmentation to
improve segmentation accuracy and robustness. A major way to encode such a
prior shape model is to use a mesh representation, which is prone to causing
self-intersection or mesh folding. Those problems require complex and expensive
algorithms to mitigate. In this paper, we propose a novel shape prior directly
embedded in the voxel grid space, based on gradient vector flows of a
pre-segmentation. The flexible and powerful prior shape representation is ready
to be extended to simultaneously segmenting multiple interacting objects with
minimum separation distance constraint. The problem is formulated as a Markov
random field problem whose exact solution can be efficiently computed with a
single minimum s-t cut in an appropriately constructed graph. The proposed
algorithm is validated on two multi-object segmentation applications: the brain
tissue segmentation in MRI images, and the bladder/prostate segmentation in CT
images. Both sets of experiments show superior or competitive performance of
the proposed method to other state-of-the-art methods.