Document Detail


Automated detection of intracranial aneurysms based on parent vessel 3D analysis.
MedLine Citation:
PMID:  20004607     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The detection of brain aneurysms plays a key role in reducing the incidence of intracranial subarachnoid hemorrhage (SAH) which carries a high rate of morbidity and mortality. The majority of non-traumatic SAH cases is caused by ruptured intracranial aneurysms and accurate detection can decrease a significant proportion of misdiagnosed cases. A scheme for automated detection of intracranial aneurysms is proposed in this study. Applied to the segmented cerebral vasculature, the method detects aneurysms as suspect regions on the vascular tree, and is designed to assist diagnosticians with their interpretations and thus reduce missed detections. In the current approach, the vessels are segmented and their medial axis is computed. Small regions along the vessels are inspected and the writhe number is introduced as a new surface descriptor to quantify how closely any given region approximates a tubular structure. Aneurysms are detected as non-tubular regions of the vascular tree. The geometric assumptions underlying the approach are investigated analytically and validated experimentally. The method is tested on 3D-rotational angiography (3D-RA) and computed tomography angiography (CTA). In our experiments, 100% sensitivity was achieved with average false positives rates of 0.66 per study on 3D-RA data and 5.36 false positive rates per study on CTA data.
Authors:
Alexandra Lauric; Eric Miller; Sarah Frisken; Adel M Malek
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Publication Detail:
Type:  Evaluation Studies; Journal Article     Date:  2009-11-11
Journal Detail:
Title:  Medical image analysis     Volume:  14     ISSN:  1361-8423     ISO Abbreviation:  Med Image Anal     Publication Date:  2010 Apr 
Date Detail:
Created Date:  2010-02-15     Completed Date:  2010-05-18     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9713490     Medline TA:  Med Image Anal     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  149-59     Citation Subset:  IM    
Copyright Information:
Copyright 2009 Elsevier B.V. All rights reserved.
Affiliation:
Tufts University, Department of Computer Science, 161 College Ave, Medford, MA 02155, USA. alauri02@cs.tufts.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Cerebral Angiography / methods*
Cerebral Arteries / radiography*
Humans
Imaging, Three-Dimensional / methods*
Intracranial Aneurysm / radiography*
Pattern Recognition, Automated / methods*
Radiographic Image Enhancement / methods
Radiographic Image Interpretation, Computer-Assisted / methods*
Reproducibility of Results
Sensitivity and Specificity

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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