MULTIPARAMETRIC AUTOMATED SEGMENTATION OF BRAIN VEINS


Background: Manual segmentation of brain vessels in a typical MR dataset is both complex and time-consuming; therefore automated approaches are actively sought for, as they also improve the reproducibility of the results.
Objectives: To present a multiparametric segmentation method (MPS) that,starting from a vessel likeliness function (Vesselness) and R2*map of the brain, applies an Expectation Maximization (EM) algorithm to the bivariate distribution of the data to classify each voxel as belonging or not to veins.
Methods: Based on the assumption that a voxel belonging to a vein has high Vesselness and R2* values, on the log-scale joint histogram of the maps, the voxels whose Vesselness is higher than 0 were assigned to 3 main classes: i) a
Gaussian distribution with low R2*and Vesselness (false positives enhanced by Vesselness); ii) a class with R2* value above a given threshold; iii) another Gaussian distribution with medium-high value of R2* and high value of
Vesselness,the last two truly corresponding to veins. Through an EM algorithm, the parameters of the 3classes were estimated and the voxels belong-ing to the 2 vessel classes were identified. The performance of the MPS was compared to the Vesselness thresholding (VT) by blindly grading on a 0-5 scale the accuracy of vascular tree depiction.
Results: The semiquantitative analysis clearly showed that MPS achieved greater accuracy in vessel display (scores 4-5 in 88% of the test-sample) than VT (scores 2-3 in 74% of the test-sample). In particular, false positives were the main pitfall of VT compared to MPS.
Conclusions: Combining the information obtained from Vesselness and R2* maps, the MPS substantially increased sensitivity and specificity of the monoparametric tresholding based on vesselness images only.

MULTIPARAMETRIC AUTOMATED SEGMENTATION OF BRAIN VEINS