Shape Based Conditional Random Fields for Segmenting Intracranial Aneurysms

Abstract

Studies have found strong correlation between the risk of rupture of intracranial aneurysms and various physical measurements on the aneurysms, such as volume, surface area, neck length, among others. Accuracy of risk prediction relies on the accuracy of these quantities, which in turn, is determined by the precision of the underlying segmentation algorithm. In this paper, we propose an algorithm for the separation of aneurysms in pathological vessels. The approach is based on conditional random fields (CRF), and exploits regional shape properties for unary, and layout constraints for pair-wise potentials to achieve a high degree of accuracy. To this end, we construct very rich rotation invariant shape descriptors, and couple them with randomized decision trees to determine posterior probabilities. These probabilities define weak priors in the unary potentials, which are also combined with strong priors determined from user interaction. Pairwise potentials are used to impose smoothness as well as spatial ordering constraints. The proposed descriptor is independent of surface orientation, and is richer than existing approaches due to attribute weighting. The conditional probability of CRF is maximized through graph-cuts, and the approach is validated with real dataset w.r.t. the groundtruth, resulting in the area overlap ratio of 88.1%. Most importantly, it successfully solves the “touching vessel leaking” problem.