ArticleMedical image analysis2022
Automated identification of pulmonary arteries and veins depicted in non-contrast chest CT scans.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Identification of pulmonary hypertension with pulmonary vascular surface volume ratio by non-contrast CT.BMC cardiovascular disorders · 2026Article
- Accurate segmentation of pulmonary arteries and veins via a human-in-the-loop framework with application in COPD.Medical & biological engineering & computing · 2026Article
- Pancreatic multi structure segmentation on CT using transformer and deep learning approaches.Discover oncology · 2026Article
- Simulation-Driven Annotation-Free Deep Learning for Automated Detection and Segmentation of Airway Mucus Plugs on Non-Contrast CT Images.Bioengineering (Basel, Switzerland) · 2026Article
- Pulmonary arterial morphological markers on non-contrast CT predicted acute exacerbations and disease progression in chronic obstructive pulmonary disease: a longitudinal cohort study.Japanese journal of radiology · 2025Article
- Beyond nodules: body composition as a biomarker for future lung cancer.European radiology · 2025Article
- A Custom Annotated Dataset for Segmentation of Pulmonary Veins, Arteries, and Airways.Scientific data · 2025Article
- Enhancing atrial fibrillation risk prediction in an observational cohort of tobacco-exposed individuals: the role of pulmonary function tests, symptom scores, and imaging.Respiratory research · 2025Observational
- Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.Clinical transplantation · 2025Article
- Predicting post-lung transplant survival in systemic sclerosis using CT-derived features from preoperative chest CT scans.European 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
- A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy.The British journal of radiology · 2025Article
- Artificial intelligence-driven quantitative analysis of CT morphological differences between chronic thromboembolic pulmonary hypertension and chronic thromboembolic disease.Quantitative imaging in medicine and surgery · 2025Article
- Predicting Postoperative Lung Cancer Recurrence and Survival Using Cox Proportional Hazards Regression and Machine Learning.Cancers · 2024Article
- Quantitative assessment of pulmonary blood flow redistribution in the classification of group 1 and group 4 pulmonary hypertension.Quantitative imaging in medicine and surgery · 2024Article
- CT-Based Lung Size Matching in Delayed Chest Closure for Systemic Sclerosis Lung Transplantation.Clinical transplantation · 2024Article
- CT-Derived Features as Predictors of Clot Burden and Resolution.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Pulmonary circulatory system characteristics are associated with future lung cancer risk.Medical physics · 2024Article
- A new segment method for pulmonary artery and vein.Health information science and systems · 2023Article
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7 authors.
Funding
Abstract
We present a novel integrative computerized solution to automatically identify and differentiate pulmonary arteries and veins depicted on chest computed tomography (CT) without iodinated contrast agents. We first identified the central extrapulmonary arteries and veins using a convolutional neural network (CNN) model. Then, a computational differential geometry method was used to automatically identify the tubular-like structures in the lungs with high densities, which we believe are the intrapulmonary vessels. Beginning with the extrapulmonary arteries and veins, we progressively traced the intrapulmonary vessels by following their skeletons and differentiated them into arteries and veins. Instead of manually labeling the numerous arteries and veins in the lungs for machine learning, this integrative strategy limits the manual effort only to the large extrapulmonary vessels. We used a dataset consisting of 120 chest CT scans acquired on different subjects using various protocols to develop, train, and test the algorithms. Our experiments on an independent test set (n = 15) showed promising performance. The computer algorithm achieved a sensitivity of ∼98% in labeling the pulmonary artery and vein branches when compared with a human expert's results, demonstrating the feasibility of our computerized solution in pulmonary artery/vein labeling.
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