Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences

Yuetan Chu, Gongning Luo, Longxi Zhou, Shaodong Cao, Guolin Ma, Xianglin Meng, Juexiao Zhou, Changchun Yang, Dexuan Xie, Dan Mu, Ricardo Henao, Gianluca Setti, Xigang Xiao, Lianming Wu, Zhaowen Qiu, Xin Gao

Abstract: Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditional methods rely on Computed Tomography Pulmonary Angiography (CTPA), which requires contrast agents with potential health risks. Non-contrast CT, a safer and more widely available approach, however, has long been considered impossible for this task. Here we propose High-abundant Pulmonary Artery-vein Segmentation (HiPaS), enabling accurate segmentation across both non-contrast CT and CTPA at multiple resolutions. HiPaS integrates spatial normalization with an iterative segmentation strategy, leveraging lower-level vessel segmentations as priors for higher-level segmentations. Trained on a multi-center dataset comprising 1073 CT volumes with manual annotations, HiPaS achieves superior performance (dice score: 91.8%, sensitivity: 98.0%) and demonstrates non-inferiority on non-contrast CT compared to CTPA. Furthermore, HiPaS enables large-scale analysis of 11,784 participants, revealing associations between vessel abundance and sex, age, and diseases, under lung-volume control. HiPaS represents a promising, non-invasive approach for clinical diagnostics and anatomical research. Pulmonary artery-vein segmentation is essential for disease diagnosis but has not been used with non-contrast CT. Here the authors developed HiPaS, a deep learning method which enables this with no inferiority to CTPA performance and a large-scale anatomical study using HiPaS reveals pulmonary vessel differences associated with sex, age, and disease.

External IDs:doi:10.1038/s41467-025-56505-6