ArticleIEEE transactions on medical imaging2018
Pulmonary Artery-Vein Classification in CT Images Using Deep Learning.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.
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Who cites it
77 citing papers in PubMed, 1 synthesis or guideline pooled it, 180 citations in OpenAlex.
- A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH).Medicina (Kaunas, Lithuania) · 2025Pooled it
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- Accurate segmentation of pulmonary arteries and veins via a human-in-the-loop framework with application in COPD.Medical & biological engineering & computing · 2026Article
- Quantitative assessment of intrapulmonary vessel volume by CTPA in vasculitis patients with pulmonary vascular involvement.BMC medical imaging · 2026Article
- Pulmonary vascular features on chest computed tomography differentially associate with adverse outcomes in smokers in COPDGene.Annals of the American Thoracic Society · 2026Article
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- Pulmonary Artery and Vein Morphology as an Imaging Biomarker for the Diagnosis of Pulmonary Hypertension.Diagnostics (Basel, Switzerland) · 2026Article
- A Custom Annotated Dataset for Segmentation of Pulmonary Veins, Arteries, and Airways.Scientific data · 2025Article
- Spatial heterogeneity and distribution of CT-Based pulmonary vascular volumes in chronic thromboembolic pulmonary hypertension.European journal of radiology · 2025Article
- Assessing pulmonary vascular remodeling in connective tissue disease-associated interstitial lung disease: a CT-based quantitative analysis.Quantitative imaging in medicine and surgery · 2025Article
- Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences.Nature communications · 2025Article
- Automated 3D Quantitative Analysis of Intrapulmonary Vessel Volume on Noncontrast CT in Healthy Individuals.Current medical imaging · 2025Article
- Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney.Scientific reports · 2024Article
- Preacinar Arterial Dilation Mediates Outcomes of Quantitative Interstitial Abnormalities in the COPDGene Study.American journal of respiratory and critical care medicine · 2024Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- Deep Learning for 3D Vascular Segmentation in Phase Contrast Tomography.Research square · 2024Article
- Brain metastasis magnetic resonance imaging-based deep learning for predicting epidermal growth factor receptor (Quantitative imaging in medicine and surgery · 2024Article
- Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment.Healthcare (Basel, Switzerland) · 2024Review
- COPD: pulmonary vascular volume associated with cardiac structure and function.The international journal of cardiovascular imaging · 2024Article
- Quantitative Assessment Characteristics of Small Pulmonary Vessel Remodelling in Populations at High Risk for COPD and Smokers Using Low-Dose CT.International journal of chronic obstructive pulmonary disease · 2024Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
Recent studies show that pulmonary vascular diseases may specifically affect arteries or veins through different physiologic mechanisms. To detect changes in the two vascular trees, physicians manually analyze the chest computed tomography (CT) image of the patients in search of abnormalities. This process is time consuming, difficult to standardize, and thus not feasible for large clinical studies or useful in real-world clinical decision making. Therefore, automatic separation of arteries and veins in CT images is becoming of great interest, as it may help physicians to accurately diagnose pathological conditions. In this paper, we present a novel, fully automatic approach to classify vessels from chest CT images into arteries and veins. The algorithm follows three main steps: first, a scale-space particles segmentation to isolate vessels; then a 3-D convolutional neural network (CNN) to obtain a first classification of vessels; finally, graph-cuts' optimization to refine the results. To justify the usage of the proposed CNN architecture, we compared different 2-D and 3-D CNNs that may use local information from bronchus- and vessel-enhanced images provided to the network with different strategies. We also compared the proposed CNN approach with a random forests (RFs) classifier. The methodology was trained and evaluated on the superior and inferior lobes of the right lung of 18 clinical cases with noncontrast chest CT scans, in comparison with manual classification. The proposed algorithm achieves an overall accuracy of 94%, which is higher than the accuracy obtained using other CNN architectures and RF. Our method was also validated with contrast-enhanced CT scans of patients with chronic thromboembolic pulmonary hypertension to demonstrate that our model generalizes well to contrast-enhanced modalities. The proposed method outperforms state-of-the-art methods, paving the way for future use of 3-D CNN for artery/vein classification in CT images.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.