Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences
This work addresses a critical problem for clinicians by enabling safer, non-contrast CT-based pulmonary artery-vein segmentation for disease diagnosis and surgical planning, representing a novel method for a known bottleneck.
The researchers tackled the challenge of accurately segmenting pulmonary arteries and veins from non-contrast CT scans, which was previously considered impossible, by developing the HiPaS framework, achieving an average dice score of 91.8% and sensitivity of 98.0% on a dataset of 1,073 CT volumes. They applied this method to an anatomical study of 11,784 participants, revealing new associations between pulmonary vessel anatomy and factors like sex, age, and disease states.
Pulmonary artery-vein segmentation is crucial for disease diagnosis 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 volumes via a super-resolution module, and then iteratively achieves segmentation results at different branch levels by utilizing the lower-level vessel segmentation as a prior for higher-level vessel segmentation. We trained and validated HiPaS on our established multi-centric dataset comprising 1,073 CT volumes with meticulous manual annotations. Both quantitative experiments and clinical evaluation demonstrated the superior performance of HiPaS, achieving an average dice score of 91.8% and a sensitivity of 98.0%. Further experiments showed 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 11,784 participants in China (six sites), discovering a new association of pulmonary vessel anatomy with sex, age, and disease states: vessel abundance suggests a significantly higher association with females than males with slightly decreasing with age, and is also influenced by certain diseases, under the controlling of lung volumes.