A Deep Learning Approach to Automate High-Resolution Blood Vessel Reconstruction on Computerized Tomography Images With or Without the Use of Contrast Agent

• DOI: 10.48550/arxiv.2002.03463
• Publication Date: 2020-01-01
• Authors: Anirudh Chandrash...; Ashok Handa; Natesh Shivakumar; Pierfrancesco Lap...; Vicente Grau; Regent Lee
• Keywords: Lumen (anatomy)

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