Open AccessA Deep Learning Pipeline to Automate High-Resolution Arterial Segmentation With or Without Intravenous Contrast
Author(s) -
Anirudh Chandrashekar,
Ashok Handa,
Natesh Shivakumar,
Pierfrancesco Lapolla,
Raman Uberoi,
Vicente Grau,
Regent Lee
Publication year - 2020
Publication title -
annals of surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.153
H-Index - 309
eISSN - 1528-1140
pISSN - 0003-4932
DOI - 10.1097/sla.0000000000004595
Subject(s) - medicine , segmentation , radiology , pipeline (software) , lumen (anatomy) , intravenous contrast , contrast (vision) , artificial intelligence , computer science , computed tomography , programming language
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.