Phantom-based evaluation of a semi-automatic segmentation algorithm for cerebral vascular structures in 3D ultrasound angiography (3D USA)

Abstract

Intraoperative ultrasound angiography (USA) provides to the neurosurgeon real-time information about the cerebral vascular network but is difficult to interpret due to the presence of noise and artifacts. A segmentation algorithm may improve the visualization of data by extracting the vascular structures only. We propose to adapt and test an existing model-based segmentation method on 3D USA data of a vascular phantom with 4 mm tube radii. The performance of the algorithm is evaluated by comparison with a gold standard (CT data) and with manual delineations. The algorithm generated a segmentation model whose radii values are overestimated of more than half of one mm in comparison with the gold standard but with more realistic geometrical features than the manual delineations.