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

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 both non-contrast CT and CTPA across various spatial resolutions. HiPaS first performs spatial normalization on raw CT scans via a super-resolution module, and then iteratively achieves segmentation results at different branch levels by utilizing the low-level vessel segmentation as a prior for high-level vessel segmentation. We trained and validated HiPaS on our established multi-centric dataset comprising 1,073 CT volumes with meticulous manual annotation. Both quantitative experiments and clinical evaluation demonstrated the superior performance of HiPaS, achieving a dice score of 91.8 98.0 segmentation on non-contrast CT compared to segmentation on CTPA. Employing HiPaS, we have conducted an anatomical study of pulmonary vasculature on 10,613 participants in China (five sites), discovering a new association between pulmonary vessel abundance and sex and age: vessel abundance is significantly higher in females than in males, and slightly decreases with age, under the controlling of lung volumes (p < 0.0001). HiPaS realizing accurate artery-vein segmentation delineates a promising avenue for clinical diagnosis and understanding pulmonary physiology in a non-invasive manner.