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Updated: Jul 5, 2026
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A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage
Published on: July 28, 2018
Cerebral arteries: fully automated segmentation from CT angiography--a feasibility study
Rashindra Manniesing1, Max A Viergever, Aad van der Lugt
1Departments of Medical Informatics and Radiology, Erasmus MC-University Medical Center Rotterdam, Dr. Molewaterplein 40/50, 3015 GE Rotterdam, the Netherlands. r.manniesing@erasmusmc.nl
Radiology
|May 20, 2008
Summary
Fully automated segmentation of cerebral arteries using computed tomographic angiography is feasible. This advanced imaging tool accurately maps the arterial cerebrovasculature, aiding in diagnosing conditions like subarachnoid hemorrhage.
Area of Science:
- Medical Imaging
- Radiology
- Neuroscience
Background:
- Subarachnoid hemorrhage diagnosis often relies on detailed imaging of cerebral arteries.
- Manual segmentation of cerebrovasculature from CT angiography can be time-consuming and complex.
Purpose of the Study:
- To evaluate the feasibility of a fully automated image postprocessing tool for segmenting the arterial cerebrovasculature.
- To assess this tool's performance in patients with subarachnoid hemorrhage using CT angiography.
Main Methods:
- A novel automated postprocessing method was developed for CT angiography.
- The method involved automatic artery detection, segmentation through the skull base, and large vein suppression.
- No additional CT scan for bone suppression was required.
Main Results:
- The automated segmentation method was successful in 83% of the tested patients.
- The mean difference between automated and manual diameter measurements was 0.0 mm +/- 0.4 mm.
- The tool demonstrated feasibility for segmenting cerebral arteries.
Conclusions:
- Fully automated segmentation of the cerebral arteries from CT angiography is achievable.
- This automated tool shows promise for efficient and accurate cerebrovasculature analysis.
- The findings support the clinical utility of automated image postprocessing in neurovascular imaging.
