Deep learning-driven pulmonary arteries and veins segmentation reveals demography-associated pulmonary vasculature anatomy

arXiv:2404.07671v1 Announce Type: new
Abstract: Pulmonary artery-vein segmentation is crucial for diagnosing pulmonary diseases and surgical planning, and is traditionally achieved by Computed Tomography Pulmonary Angiography (CTPA). However, concerns regarding adverse health effects from contrast agents used in CTPA have constrained its clinical utility. In contrast, identifying arteries and veins using non-contrast CT, a conventional and low-cost clinical examination routine, has long been considered impossible. Here we propose a High-abundant Pulmonary Artery-vein Segmentation (HiPaS) framework achieving accurate artery-vein segmentation on …

abstract agents arxiv clinical concerns contrast cs.cv deep learning diseases effects health however planning segmentation type utility