  Existing methods to reconstruct vascular structures from a computed
tomography (CT) angiogram rely on injection of intravenous contrast to enhance
the radio-density within the vessel lumen. However, pathological changes can be
present in the blood lumen, vessel wall or a combination of both that prevent
accurate reconstruction. In the example of aortic aneurysmal disease, a blood
clot or thrombus adherent to the aortic wall within the expanding aneurysmal
sac is present in 70-80% of cases. These deformations prevent the automatic
extraction of vital clinically relevant information by current methods. In this
study, we implemented a modified U-Net architecture with attention-gating to
establish a high-throughput and automated segmentation pipeline of pathological
blood vessels in CT images acquired with or without the use of a contrast
agent. Twenty-six patients with paired non-contrast and contrast-enhanced CT
images within the ongoing Oxford Abdominal Aortic Aneurysm (OxAAA) study were
randomly selected, manually annotated and used for model training and
evaluation (13/13). Data augmentation methods were implemented to diversify the
training data set in a ratio of 10:1. The performance of our Attention-based
U-Net in extracting both the inner lumen and the outer wall of the aortic
aneurysm from CT angiograms (CTA) was compared against a generic 3-D U-Net and
displayed superior results. Subsequent implementation of this network
architecture within the aortic segmentation pipeline from both
contrast-enhanced CTA and non-contrast CT images has allowed for accurate and
efficient extraction of the entire aortic volume. This extracted volume can be
used to standardize current methods of aneurysmal disease management and sets
the foundation for subsequent complex geometric and morphological analysis.
Furthermore, the proposed pipeline can be extended to other vascular
pathologies.
