Optimal Multi-Object Segmentation with Novel Gradient Vector Flow Based Shape Priors

TL;DR

This paper introduces a novel voxel grid-based shape prior using gradient vector flows for multi-object segmentation, enabling efficient and accurate segmentation of interacting objects in medical images.

Contribution

The paper presents a new shape prior embedded in voxel space that improves multi-object segmentation and can be efficiently solved with a single min s-t cut.

Findings

Outperforms state-of-the-art methods in brain tissue segmentation.

Achieves competitive results in bladder/prostate segmentation.

Efficiently handles multiple interacting objects with minimal separation constraints.

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.