ArticleNature communications2025
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Interpretable dynamic quantitative vascular morphometry features using SHAP for anti-angiogenic therapy response prediction.Science advances · 2026Article
- A generative adversarial network model for improved three-dimensional mapping of pulmonary arteries and veins from non-contrast computed tomography in sublobar resection planning.Quantitative imaging in medicine and surgery · 2026Article
- SwiftMSeg: lightweight multi-scale local-global context modeling with transformer for medical image segmentation.Scientific reports · 2026Article
- Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026Review
- Topology-preserving, diameter-specific framework for conjunctival vessel segmentation and tortuosity analysis in diabetes.Scientific reports · 2026Article
- Accurate segmentation of pulmonary arteries and veins via a human-in-the-loop framework with application in COPD.Medical & biological engineering & computing · 2026Article
- CT-based AI system for quantitative and integrated management of acute respiratory distress syndrome in critical care.NPJ digital medicine · 2026Article
- 3D Automated Segmentation of Bronchial Abnormalities on Ultrashort Echo Time MRI: A Quantitative MR Outcome in Cystic Fibrosis.Journal of magnetic resonance imaging : JMRI · 2026Article
- Pulmonary Artery and Vein Morphology as an Imaging Biomarker for the Diagnosis of Pulmonary Hypertension.Diagnostics (Basel, Switzerland) · 2026Article
- Anthropometry, sex, and age at diagnosis affect pulmonary blood volume quantification from computed tomography pulmonary angiography in pulmonary hypertension assessment.Frontiers in cardiovascular medicine · 2026Article
- Lesion feature enhancement and boundary-aware fusion for pulmonary embolism recognition on CT images.Frontiers in medicine · 2026Article
- A deep learning model for the interpretable identification of pulmonary thromboembolism from computed tomography pulmonary angiography.Frontiers in cardiovascular medicine · 2026Article
- AI-based prediction of pulmonary hypertension in COPD patients with cor pulmonale using clinical and CT features.Frontiers in medicine · 2026Article
- Evaluation of the impact of cardiopulmonary rehabilitation exercise training on cardiopulmonary function in patients with chronic obstructive pulmonary disease complicated by unstable angina pectoris using a hierarchical deep learning CT image model.Frontiers in physiology · 2026Article
- A Custom Annotated Dataset for Segmentation of Pulmonary Veins, Arteries, and Airways.Scientific data · 2025Article
- AI-Enhanced Deep Learning Framework for Pulmonary Embolism Detection in CT Angiography.Bioengineering (Basel, Switzerland) · 2025Article
- Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences.Nature communications · 2025Article
- The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection.Frontiers in medicine · 2025Review
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16 authors.
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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.
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