{'fraction_non_alphanumeric': 0.018518518518518517, 'fraction_numerical': 0.005401234567901234, 'mean_word_length': 5.389162561576355, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Evaluation of a semi-automatic segmentation algorithm in 3D intraoperative ultrasound brain angiography

Abstract In this work, we adapted a semi-automatic segmentation algorithm for vascular structures to extract cerebral blood vessels in the 3D intraoperative contrast-enhanced ultrasound angiographic (3D-iUSA) data of the brain. We quantitatively evaluated the segmentation method with a physical vascular phantom. The geometrical features of the segmentation model generated by the algorithm were compared with the theoretical tube values and manual delineations provided by observers. For a silicon tube with a radius of 2 mm, the results showed that the algorithm overestimated the lumen radii values by about 1 mm, representing one voxel in the 3D-iUSA data. However, the observers were more hindered by noise and artifacts in the data, resulting in a larger overestimation of the tube lumen (twice the reference size). The first results on 3D-iUSA patient data showed that the algorithm could correctly restitute the main vascular segments with realistic geometrical features data, despite noise, artifacts and unclear blood vessel borders. A future aim of this work is to provide neurosurgeons with a visualization tool to navigate through the brain during aneurysm clipping operations.