Combining machine learning and artery characterization to identify the main bifurcations in 3D vascular trees

Ibtissam Essadik; Anass Nouri; Nathan Lauzeral; Raja Touahni; Romain Bourcier; Florent Autrusseau

doi:10.1117/12.2605459

4 April 2022 Combining machine learning and artery characterization to identify the main bifurcations in 3D vascular trees

Ibtissam Essadik, Anass Nouri, Nathan Lauzeral, Raja Touahni, Romain Bourcier, Florent Autrusseau

Author Affiliations +

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 120360E (2022) https://doi.org/10.1117/12.2605459
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

The cerebral vascular system is constituted by all the arteries and veins irrigating the brain. This vascular tree starts from two pairs of arteries, the vertebral arteries and the internal carotid arteries. These latter divide into a circular shape being called the Circle of Willis (CoW). There is considerable variability in the structure of the CoW among patients. The CoW can host various vascular diseases, among which intracranial aneurysms are of particular importance because their occurrence, or more precisely their rupture, can be devastating. Intracranial aneurysms often occur at the bifurcations of the arterial tree (saccular aneurysms), as a bulge in the vessel wall. It is crucial to recognize and monitor such aneurysms. Anatomical identification of the bifurcations of the CoW can be of great help to establish a diagnosis or to plan a surgical operation. In this study, we propose an automatic solution to categorize the vascular anatomy of the CoW in 3D volumes by identifying its main constituting bifurcations. Our solution combines machine learning and a multivariate analysis (Linear Discriminant Analysis: LDA). The LDA works as a classifier and reduces the dimensionality of the dataset by transforming the selected features in a lower dimensional space. This work is a preliminary study prior to moving to human cerebrovascular images. We evaluate the proposed method using several machine learning techniques combined with a leave-one-out validation applied on a set of 30 synthetic vascular images as well as 30 mouse cerebral vasculatures.

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Citation Download Citation

Ibtissam Essadik, Anass Nouri, Nathan Lauzeral, Raja Touahni, Romain Bourcier, and Florent Autrusseau "Combining machine learning and artery characterization to identify the main bifurcations in 3D vascular trees", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 120360E (4 April 2022); https://doi.org/10.1117/12.2605459

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