Optimal Multi-Object Segmentation with Novel Gradient Vector Flow Based Shape Priors
This work addresses segmentation accuracy and robustness issues in medical imaging, particularly for multi-object scenarios, representing an incremental improvement over existing shape prior methods.
The paper tackled the problem of self-intersection and mesh folding in shape priors for medical image segmentation by proposing a novel gradient vector flow-based shape prior in voxel grid space, enabling efficient multi-object segmentation with constraints, and achieved superior or competitive performance in brain tissue and bladder/prostate segmentation experiments.
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.