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Proceedings Paper

Computer-assisted adjuncts for aneurysmal morphologic assessment: toward more precise and accurate approaches
Author(s): Hamidreza Rajabzadeh-Oghaz; Nicole Varble; Jason M. Davies; Ashkan Mowla; Hakeem J. Shakir; Ashish Sonig; Hussain Shallwani; Kenneth V. Snyder; Elad I. Levy; Adnan H. Siddiqui; Hui Meng
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Paper Abstract

Neurosurgeons currently base most of their treatment decisions for intracranial aneurysms (IAs) on morphological measurements made manually from 2D angiographic images. These measurements tend to be inaccurate because 2D measurements cannot capture the complex geometry of IAs and because manual measurements are variable depending on the clinician’s experience and opinion. Incorrect morphological measurements may lead to inappropriate treatment strategies. In order to improve the accuracy and consistency of morphological analysis of IAs, we have developed an image-based computational tool, AView. In this study, we quantified the accuracy of computer-assisted adjuncts of AView for aneurysmal morphologic assessment by performing measurement on spheres of known size and anatomical IA models. AView has an average morphological error of 0.56% in size and 2.1% in volume measurement. We also investigate the clinical utility of this tool on a retrospective clinical dataset and compare size and neck diameter measurement between 2D manual and 3D computer-assisted measurement. The average error was 22% and 30% in the manual measurement of size and aneurysm neck diameter, respectively. Inaccuracies due to manual measurements could therefore lead to wrong treatment decisions in 44% and inappropriate treatment strategies in 33% of the IAs. Furthermore, computer-assisted analysis of IAs improves the consistency in measurement among clinicians by 62% in size and 82% in neck diameter measurement. We conclude that AView dramatically improves accuracy for morphological analysis. These results illustrate the necessity of a computer-assisted approach for the morphological analysis of IAs.

Paper Details

Date Published: 3 March 2017
PDF: 8 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341C (3 March 2017); doi: 10.1117/12.2255553
Show Author Affiliations
Hamidreza Rajabzadeh-Oghaz, Univ. at Buffalo (United States)
Nicole Varble, Univ. at Buffalo (United States)
Jason M. Davies, Univ. at Buffalo (United States)
Ashkan Mowla, Univ. at Buffalo (United States)
Hakeem J. Shakir, Univ. at Buffalo (United States)
Ashish Sonig, Univ. at Buffalo (United States)
Hussain Shallwani, Univ. at Buffalo (United States)
Kenneth V. Snyder, Univ. at Buffalo (United States)
Elad I. Levy, Univ. at Buffalo (United States)
Adnan H. Siddiqui, Univ. at Buffalo (United States)
Hui Meng, Univ. at Buffalo (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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