Accessible Unlicensed Requires Authentication Published by De Gruyter May 4, 2013

Evaluation of a semi-automatic segmentation algorithm in 3D intraoperative ultrasound brain angiography

Claire Chalopin, Karl Krissian, Jürgen Meixensberger, Andrea Müns, Felix Arlt and Dirk Lindner


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.

Corresponding author: Claire Chalopin, Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Semmelweisstr. 14, 04103 Leipzig, Germany, Phone: +49 (0) 341 97 17508 or +49 (0) 341 97 12012, Fax: +49 (0) 341 97 12009


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Received: 2012-6-7
Accepted: 2013-4-3
Published Online: 2013-05-04
Published in Print: 2013-06-01

©2013 by Walter de Gruyter Berlin Boston