1IRCCS SDN, Naples; 2Department of Bioengineering, Politecnico di Milano, Milan; 3Institute of Biostructure and Bioimaging,National Research Council, Naples; 4Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy
References: Not available.
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
No results.
MULTIPARAMETRIC AUTOMATED SEGMENTATION OF BRAIN VEINS