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 Riferimenti: Not available. Non disponibili.
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 Vesselnessand 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. Nessun risultato.
MULTIPARAMETRIC AUTOMATED SEGMENTATION OF BRAIN VEINS
4 Records (3 escludendo Abstract e Conferenze). Impact factor totale: 10.662 (10.662 escludendo Abstract e Conferenze). Impact factor a 5 anni totale: 9.81 (9.81 escludendo Abstract e Conferenze).
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