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 () and Xin Gao ()
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Yuetan Chu: King Abdullah University of Science and Technology (KAUST)
Gongning Luo: King Abdullah University of Science and Technology (KAUST)
Longxi Zhou: King Abdullah University of Science and Technology (KAUST)
Shaodong Cao: The Fourth Hospital of Harbin Medical University
Guolin Ma: China-Japan Friendship Hospital
Xianglin Meng: The First Affiliated Hospital of Harbin Medical University
Juexiao Zhou: King Abdullah University of Science and Technology (KAUST)
Changchun Yang: King Abdullah University of Science and Technology (KAUST)
Dexuan Xie: The First Affiliated Hospital of Harbin Medical University
Dan Mu: Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Ricardo Henao: King Abdullah University of Science and Technology (KAUST)
Gianluca Setti: King Abdullah University of Science and Technology (KAUST)
Xigang Xiao: The First Affiliated Hospital of Harbin Medical University
Lianming Wu: Shanghai Jiao Tong University
Zhaowen Qiu: Northeast Forestry University
Xin Gao: King Abdullah University of Science and Technology (KAUST)

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-56505-6

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