Fast vascular skeleton extraction algorithm

Characterization of 3D bifurcations in micro-scan and MRA-TOF images of cerebral vasculature for prediction of intra-cranial aneurysms

An aneurysm is a vascular disorder where ballooning may form in a weakened section of the wall in the blood vessel. The swelling of the aneurysm may lead to its rupture. Intra-cranial aneurysms are the ones presenting the higher risks. If ruptured, the aneurysm may induce a subarachnoid haemorrhage which could lead to premature death or permanent disability. In this study, we are interested in locating and characterizing the bifurcations of the cerebral vascular tree. We use a 3D skeletonization combined with a graph-based approach to detect the bifurcations. In this work, we thus propose a full geometric characterisation of the bifurcations and related arteries. Aside from any genetic predisposition and environmental risk factors, the geometry of the brain vasculature may influence the chance of aneurysm formation. Among the main achievements, in this paper, we propose accurate, predictive 3D measurements of the bifurcations and we furthermore estimate the risk of occurrence of an aneurysm on a given bifurcation.

Automated Anatomy-Based Tracking of Systemic Arteries in Arbitrary Field-of-View CTA Scans

We propose an automated pipeline for vessel centerline extraction in 3-D computed tomography angiography (CTA) scans with arbitrary fields of view. The principal steps of the pipeline are body part detection, candidate seed selection, segment tracking, which includes centerline extraction, and vessel tree growing. The final tree-growing step can be instantiated in either a semi- or fully automated fashion. The fully automated initialization is carried out using a vessel position regression algorithm. Both semi-and fully automated methods were evaluated on 30 CTA scans comprising neck, abdominal, and leg arteries in multiple fields of view. High detection rates and centerline accuracy values for 38 distinct vessels demonstrate the effectiveness of our approach.

Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation

Vascular segmentation plays an important role in the assessment of peripheral arterial disease. The segmentation is very challenging especially for arteries with severe stenosis or complete occlusion. We present a cascading algorithm for vascular centerline tree detection specializing in detecting centerlines in diseased peripheral arteries. It takes a three-dimensional computed tomography angiography (CTA) volume and returns a vascular centerline tree, which can be used for accelerating and facilitating the vascular segmentation. The algorithm consists of four levels, two of which detect healthy arteries of varying sizes and two that specialize in different types of vascular pathology: severe calcification and occlusion. We perform four main steps at each level: appropriate parameters for each level are selected automatically, a set of centrally located voxels is detected, these voxels are connected together based on the connection criteria, and the resulting centerline tree is corrected from spurious branches. The proposed method was tested on 25 CTA scans of the lower limbs, achieving an average overlap rate of 89% and an average detection rate of 82%. The average execution time using four CPU cores was 70 s, and the technique was successful also in detecting very distal artery branches, e.g., in the foot.