Deep learning-driven pulmonary arteries and veins segmentation reveals
  demography-associated pulmonary vasculature anatomy
Yuetan Chu Gongning Luo Longxi Zhou Shaodong Cao Guolin Ma Xianglin Meng Juexiao Zhou Changchun Yang Dexuan Xie Ricardo Henao Xigang Xiao Lianming Wu Zhaowen Qiu Xin Gao
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% and a sensitivity of
98.0%. Further experiments demonstrated the non-inferiority of HiPaS
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.