Relationship Between Cerebral Aneurysm Development and Cerebral Artery Shape

Marin Yasugi; Belayat Hossain; Manabu Nii; Syoji Kobashi

doi:10.20965/jaciii.2018.p0249

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JACIII Vol.22 No.2 pp. 249-255

doi: 10.20965/jaciii.2018.p0249

(2018)

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Relationship Between Cerebral Aneurysm Development and Cerebral Artery Shape

Marin Yasugi, Belayat Hossain, Manabu Nii, and Syoji Kobashi

Graduate School of Engineering, University of Hyogo
2167 Shosha, Himeji, Hyogo 671-2280, Japan

Received:

June 1, 2017

Accepted:

January 11, 2018

Published:

March 20, 2018

Keywords:

aneurysm occurrence prediction, magnetic resonance imaging, support vector machines, circle of Willis

Abstract

Lifestyle and genetics are known to be the major factors causing cerebral aneurysms, but some studies suggest that the shape of cerebral arteries might be correlated with the risk of aneurysm occurrence. This study focuses on the shape of cerebral arteries where cerebral aneurysms tend to occur. First, it extracts the shape feature of the cerebral artery ring, which is a predilection site of cerebral aneurysm, from 3-D magnetic resonance angiography images, and calculates four types of shape feature vectors – 3-D shape, bifurcation angle, degree of meandering, and direction of the branch points. Then, it estimates the risk of cerebral aneurysms occurring, based on the extracted features using support vector machine. To validate the proposed method, we conducted a leave-one-out cross validation test using 80 subjects (40 subjects with and 40 subjects without cerebral aneurysms). The method using a 3-D artery shape achieved 75% sensitivity and 75% specificity; the one using the bifurcation angle showed 47% sensitivity and 41% specificity. The method using the degree of meandering showed 55% sensitivity and 53% specificity, and the one that used the direction of the six branch points showed 30% sensitivity and 27% specificity. These results show that the 3-D artery shape could be a possible indicator for predicting the risk of developing cerebral aneurysms.

Cite this article as:

M. Yasugi, B. Hossain, M. Nii, and S. Kobashi, “Relationship Between Cerebral Aneurysm Development and Cerebral Artery Shape,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.2, pp. 249-255, 2018.

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References

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[2] S. Kobashi, N. Kamiura, Y. Hata, and F. Miyawaki, “Volume Quantization Based Neural Network Approach to 3D MR Angiography Image Segmentation,” Image and Vision Computing, Vol.19, No.4, pp. 185-193, 2001.
[3] UCAS Japan Investigators, “The natural course of unruptured cerebral aneurysms in a Japanese cohort,” The New England J. of Medicine, Vol.366, No.24, pp. 2474-2482, 2012.
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[15] S. Kobashi, K. Kondo, and Y. Hata, “Computer-Aided Diagnosis of Intracranial Aneurysms in MRA Images with Case-Based Reasoning,” IEICE Trans. on Inf. and Sys., Vol.E89-D, No.1, pp. 340-350, 2006.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] K. Ogasawara, T. Kayama, Y. Sakurai, H. Niizuma, H. Sato, and A. Nishino, “Clinical analysis of etiology of spontaneous subarachnoid hemorrhage diagnosed by computed tomography,” No To Shinkei, Vol.42, No.4, pp. 399-404, 1990.

[2] [2] S. Kobashi, N. Kamiura, Y. Hata, and F. Miyawaki, “Volume Quantization Based Neural Network Approach to 3D MR Angiography Image Segmentation,” Image and Vision Computing, Vol.19, No.4, pp. 185-193, 2001.

[3] [3] UCAS Japan Investigators, “The natural course of unruptured cerebral aneurysms in a Japanese cohort,” The New England J. of Medicine, Vol.366, No.24, pp. 2474-2482, 2012.

[4] [4] A. J. Molyneux, R. S. Kerr, L. M. Yu, M. Clarke, M. Sneade, J. A. Yarnold, and P. Sandercock, “International subarachnoid aneurysm trial (ISAT) of neurosurgical clipping versus endovascular coiling in 2143 patients with ruptured intracranial aneurysms: a randomised comparison of effect on survival, dependency, seizures, rebleeding, subgroups, and aneurysm occlusion,” The Lancet, Vol.366, No.9488, pp. 809-817, 2005.

[5] [5] G. Ailawadi, J. L. Eliason, and G. R. Upchurch Jr., “Current concepts in the pathogenesis of abdominal aortic aneurysm,” J. of Vascular Surgery, Vol.38, No.3, pp. 584-588, 2003.

[6] [6] R. Strauss, G. Oszkinis, and R. Staniszewski, “SEPP1 gene variants and abdominal aortic aneurysm: gene association in relation to metabolic risk factors and peripheral arterial disease coexistence,” Scientific Reports, Vol.4, No.7061, 2014.

[7] [7] B. Ma, R. E. Harbaugh, and M. L. Raghavan, “Three-dimensional geometric characterization of cerebral aneurysms,” Annuals of Biomedical Engineering, Vol.32, No.2, pp. 264-273, 2004.

[8] [8] J. P. Greving et al., “Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies,” Lancet Neurology, Vol.13, No.1, pp. 59-66, 2014.

[9] [9] S. Tominari et al., “Prediction model for 3-year rupture risk of unruptured cerebral aneurysms in Japanese patients,” Annals of Neurology, Vol.77, No.6, pp. 1050-1059, 2015.

[10] [10] Z. Watanabe, et al., “Comparison of Rates of Growth between Unruptured and Ruptured Aneurysms Using Magnetic Resonance Angiography,” J. of Stroke and Cerebrovascular Diseases, Vol.26, No.12, pp. 2849-2854, 2017.

[11] [11] A. Ho, A. Mouminah, and R. Du “Posterior cerebral artery angle and the rupture of basilar tip aneurysms,” PLoS ONE, Vol.9, No.10, 2014.

[12] [12] J. Talairach and P. Tournoux, “Co-Planar Stereotaxic Atlas of the Human Brain,” Georg Thieme Verlag, New York, 1988.

[13] [13] M. E. Bowers, N. Trinh, G. A. Tung, J. J. Crisco, B. B. Kimia, and B. C. Fleming, “Quantitative MR imaging using “LiveWire” to measure tibiofemoral articular cartilage thickness,” Osteoarthritis and Cartilage, Vol.16, No.10, 2008.

[14] [14] Y. Chang and C. Lin, “Feature Ranking Using Linear SVM,” JMLR Proc., Vol.3, pp. 53-64, 2008.

[15] [15] S. Kobashi, K. Kondo, and Y. Hata, “Computer-Aided Diagnosis of Intracranial Aneurysms in MRA Images with Case-Based Reasoning,” IEICE Trans. on Inf. and Sys., Vol.E89-D, No.1, pp. 340-350, 2006.