A Deep Learning Pipeline to Automate High-Resolution Arterial Segmentation With or Without Intravenous Contrast.

ABSTRACT:

Background

Existing methods to reconstruct vascular structures from a computerized tomography (CT) angiogram rely on contrast injection to enhance the radio-density within the vessel lumen. However, pathological changes in the vasculature may be present that prevent accurate reconstruction. In aortic aneurysmal disease, a thrombus adherent to the aortic wall within the expanding aneurysmal sac is present in >90% of cases. These deformations prevent the automatic extraction of vital clinical information by existing image reconstruction methods.

Aim

In this study, a deep learning architecture consisting of a modified U-Net with attention-gating was implemented to establish a high-throughput and automated segmentation pipeline of pathological blood vessels in CT images acquired

SUBMITTER: Chandrashekar A 

PROVIDER: S-EPMC9645535 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature