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.
In this work, we aim to accurately segment the cerebral vasculature on MRI-TOF images. This study is part of a wider project1 in which we intend to characterize the arterial bifurcations in order to estimate the risk of occurrence of intra-cranial aneurysms (ICA). However, a very accurate segmentation of the vasculature is needed along the Circle of Willis (as this is where most of Intra-Cranial Aneurysms occur) prior to launch the bifurcation characterization. An imprecise segmentation of the Circle of Willis will inevitably lead to a deficient characterization, and thus an erroneous ICA risk estimation. This study was motivated by the lack of efficiency of various State of the Art segmentation methods. In this work, we try to mimic the behavior of the Human Visual System in order to correctly segment the Circle of Willis on TOF imaging of the brain. When Neuroradiologists diagnose an aneurysm on an MRI volume, they modulate the image contrast and luminance so that the vasculature is highlighted within the image. In this work, we first consider the display monitor behavior and we exploit a model that mimics the perception of contrasts by a human observer, in order to accentuate the vasculature for the last segmentation step. Indeed, thanks to this perceptual contrast enhancement, the amplitude of the vasculature moves beyond the rest of the image (parenchyma, cerebrospinal fluid,· · ·) this perceptual contrast stretching then allows to simplify the final thresholding step.
KEYWORDS: Aneurysms, Arteries, Blood vessels, 3D metrology, 3D acquisition, Brain, Blood circulation, Computed tomography, 3D image processing, Magnetic resonance imaging
An aneurysm is a vascular disorder represented by a ballooning of a blood vessel. The blood vessel’s wall is distorted by the blood flow, and a bulge forms there. When ruptured, the aneurysm may cause irreversible damage and could even lead to premature death. Intra-cranial aneurysms are the ones presenting the higher risks. In this work, thanks to a graph based approach, we detect the bifurcations located on the circle of Willis within brain mice cerebral vasculature. Once properly located in the 3D stack, we characterize the cerebral arteries bifurcations, i.e. we gather several properties of the bifurcation, such as their angles, or area cross section, in order to further estimate geometrical patterns that can favor the risk of occurrence of an intra-cranial aneurysm. Effectively, apart from genetic predisposition, and environmental risk factors (high blood pressure, smoking habits, ...) the anatomical disposition of the brain vasculature may influence the chances of an aneurysm to form. Our objectives in this paper is to obtain accurate measurements on the 3D bifurcations.
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