Journal article
Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms
Computerized Medical Imaging and Graphics, Vol.89, pp.1-12
2021
PMID: 33690001
Abstract
Unruptured intracranial aneurysms (UIAs) are prevalent neurovascular anomalies which, in rare circumstances, rupture to cause a catastrophic subarachnoid haemorrhage. Although surgical management can reduce rupture risk, the majority of UIAs exist undiscovered until rupture. Current clinical practice in the detection of UIAs relies heavily on manual radiological review of standard imaging modalities. Recent computer-aided UIA diagnoses can sensitively detect and measure UIAs within cranial angiograms but remain limited to low specificities whose output also requires considerable radiologist interpretation not amenable to broad screening efforts. To address these limitations, we have developed a novel automatic pipeline algorithm which inputs medical images and outputs detected UIAs by characterising single-voxel morphometry of segmented neurovasculature. Once neurovascular anatomy of a specified resolution is segmented, correlations between voxel-specific morphometries are estimated and spatially-clustered outliers are identified as UIA candidates. Our automated solution detects UIAs within magnetic resonance angiograms (MRA) at unmatched 86% specificity and 81% sensitivity using 3 min on a conventional laptop. Our approach does not rely on interpatient comparisons or training datasets which could be difficult to amass and process for rare incidentally discovered UIAs within large MRA files, and in doing so, is versatile to user-defined segmentation quality, to detection sensitivity, and across a range of imaging resolutions and modalities. We propose this method as a unique tool to aid UIA screening, characterisation of abnormal vasculature in at-risk patients, morphometry-based rupture risk prediction, and identification of other vascular abnormalities.
Details
- Title
- Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms
- Authors
- Mark C. Allenby (Corresponding Author) - Queensland University of TechnologyEe Shern Liang (Author) - University of QueenslandJames Harvey (Author) - University of QueenslandMaria A. Woodruff (Author) - Queensland University of TechnologyMarita Prior (Author) - University of QueenslandCraig D. Winter (Author) - University of QueenslandDavid Alonso-Caneiro (Author) - Queensland University of Technology
- Publication details
- Computerized Medical Imaging and Graphics, Vol.89, pp.1-12
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.compmedimag.2021.101888
- ISSN
- 1879-0771
- PMID
- 33690001
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99972396602621
- Output Type
- Journal article
Metrics
3 Record Views
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- Collaboration types
- Domestic collaboration
- Web Of Science research areas
- Engineering, Biomedical
- Radiology, Nuclear Medicine & Medical Imaging
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Source: InCites