Key result
An intensity-based region-growing segmentation algorithm for the carotid artery bifurcation achieved a Dice similarity coefficient of 93.6% ± 3.5% compared to manual radiologist findings.
The proposed region-growing segmentation algorithm provides reliable and smooth extraction of the carotid artery bifurcation from CTA data, comparable to state-of-the-art methods.
May streamline carotid CTA analysis; leaves open larger validation before clinical integration.
The original contribution is to propose an intensity-based segmentation algorithm for extracting the carotid artery bifurcation region and validate the proposed solution on real patients’ CTA data. The proposed homogeneity criteria allow the production of locally smooth segmentations and prevent excessive growth into neighboring tissues of similar densities. The obtained segmentation results are compared to manual findings of a radiologist and measured with the Dice similarity coefficient (D si ). This technique has been shown to be a reliable tool as effective as top state-of-the-art methods (D si =93.6%±3.5% ).
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Ogiela et al. (2013) studied Carotid artery bifurcation segmentation. Intensity-based segmentation algorithm (region-growing approach) vs. Manual findings of a radiologist was evaluated on Dice similarity coefficient (Dsi). An intensity-based region-growing segmentation algorithm for the carotid artery bifurcation achieved a Dice similarity coefficient of 93.6% ± 3.5% compared to manual radiologist findings.
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