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

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 phan...

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Bibliographic Details
Published in:Biomedizinische Technik Vol. 58; no. 3; p. 293
Main Authors: Chalopin, Claire, Krissian, Karl, Meixensberger, Jürgen, Müns, Andrea, Arlt, Felix, Lindner, Dirk
Format: Journal Article
Language:English
Published: Germany 01.06.2013
Subjects:
Algorithms
Artificial Intelligence
Blood Flow Velocity - physiology
Cerebral Arteries - diagnostic imaging
Cerebral Arteries - physiology
Cerebrovascular Circulation - physiology
Echoencephalography - instrumentation
Echoencephalography - methods
Imaging, Three-Dimensional - methods
Monitoring, Intraoperative - methods
Pattern Recognition, Automated - methods
Phantoms, Imaging
Reproducibility of Results
Sensitivity and Specificity
Surgery, Computer-Assisted - methods
ISSN:1862-278X, 1862-278X
Online Access:Get more information
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Summary: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.
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ISSN:1862-278X
1862-278X
DOI:10.1515/bmt-2012-0089