Automated Segmentation of the Carotid Bifurcation using Region Growing and Support Vector Machines

Roughly 1 in 40 deaths worldwide are caused by strokes resulting from emboli that reach the brain from ruptured atherosclerotic plaques in the carotid artery. Segmentation of the carotid artery bifurcation in MR is necessary enables further analysis. Unfortunately, this is a slow and difficult task that is often performed manually. Two segmentation methods, one based on Region Growing (RG), and one using Support Vector Machines (SVM), were implemented for segmenting the carotid bifurcation in contrast-enhanced MR Angiograms (CE-MRA). Both methods were tested quantitatively, against ground truth segmentations using the DICE and true-positive ratio (TPR) and were also scored qualitatively using visual inspection. Both methods scored highly (RG 0.890 ± 0.022, SVM 0.890 ± 0.022) using the DICE score and true-positive ratio (RG 0.938 ± 0.026, SVM 0.931 ± 0.285). During qualitative assessments, RG and SVM both scored highly with median score 4/5.