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

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.

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Publication Details

Journal
Nature Communications
Published
2025-03-06
DOI
https://doi.org/10.1038/s41467-025-56505-6
Citations
10
Primary Topic
Pulmonary Hypertension Research and Treatments
Type
article
Field-Weighted Citation Impact
15.18

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article

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

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10 citations
Nature Communications
Pulmonary Hypertension Research and Treatments
15.18
article

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

Lian‐Ming Wu, Longxi Zhou, Xigang Xiao, Guolin Ma, Zhaowen Qiu, Ricardo Henao, Gianluca Setti, Xie De-xuan, Juexiao Zhou, Xianglin Meng, Shaodong Cao, Gongning Luo, Xin D. Gao, Changchun Yang, Yuetan Chu, Dan Mu
article en
10 citations

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.

Nature CommunicationsVol. 16(1)
Harbin Medical University (CN), Shanghai Jiao Tong University (CN), Renji Hospital (CN), China-Japan Friendship Hospital (CN), Nanjing Drum Tower Hospital (CN), Fourth Affiliated Hospital of Harbin Medical University (CN), First Affiliated Hospital of Harbin Medical University (CN), Northeast Forestry University (CN), King Abdullah University of Science and Technology (SA)
King Abdullah University of Science and Technology
Openalex Percentile: Top 1%
Pulmonary Hypertension Research and Treatments
15.18
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