ArticleMedical image analysis2019
A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images.
Article in Medical image analysis, 2019. 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, 41 citations in OpenAlex.
- Article
- Lumbar paraspinal muscle quality and disability in older adults: insights from the SarcoSpine cohort.BMC geriatrics · 2026Observational
- A Custom Annotated Dataset for Segmentation of Pulmonary Veins, Arteries, and Airways.Scientific data · 2025Article
- Artificial intelligence-based pulmonary vessel segmentation: an opportunity for automated three-dimensional planning of lung segmentectomy.Interdisciplinary cardiovascular and thoracic surgery · 2025Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- Assessing visibility and bone changes of spinal metastases in CT scans: a comprehensive analysis across diverse cancer types.Skeletal radiology · 2024Article
- Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia.Scientific reports · 2024Article
- Super High Contrast USPIO-Enhanced Cerebrovascular Angiography Using Ultrashort Time-to-Echo MRI.International journal of biomedical imaging · 2024Article
- A new segment method for pulmonary artery and vein.Health information science and systems · 2023Article
- Automated detection and segmentation of pulmonary embolisms on computed tomography pulmonary angiography (CTPA) using deep learning but without manual outlining.Medical image analysis · 2023Article
- Automatic pulmonary artery-vein separation in CT images using a twin-pipe network and topology reconstruction.PeerJ. Computer science · 2023Article
- Natural aging course of lumbar extensor muscle mass and strength in community-dwelling older women: a 1-year prospective observational study.Aging clinical and experimental research · 2022Observational
- Automated identification of pulmonary arteries and veins depicted in non-contrast chest CT scans.Medical image analysis · 2022Article
- Unified Deep Learning-Based Mouse Brain MR Segmentation: Template-Based Individual Brain Positron Emission Tomography Volumes-of-Interest Generation Without Spatial Normalization in Mouse Alzheimer Model.Frontiers in aging neuroscience · 2022Article
- MEDAS: an open-source platform as a service to help break the walls between medicine and informatics.Neural computing & applications · 2022Article
- A Pulmonary Artery-Vein Separation Algorithm Based on the Relationship between Subtrees Information.Journal of healthcare engineering · 2021Article
- Cerebral Artery and Vein Segmentation in Four-dimensional CT Angiography Using Convolutional Neural Networks.Radiology. Artificial intelligence · 2020Article
- Artificial Intelligence in COPD: New Venues to Study a Complex Disease.Barcelona respiratory network reviewsArticle
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Lung vessel segmentation has been widely explored by the biomedical image processing community; however, the differentiation of arterial from venous irrigation is still a challenge. Pulmonary artery-vein (AV) segmentation using computed tomography (CT) is growing in importance owing to its undeniable utility in multiple cardiopulmonary pathological states, especially those implying vascular remodelling, allowing the study of both flow systems separately. We present a new framework to approach the separation of tree-like structures using local information and a specifically designed graph-cut methodology that ensures connectivity as well as the spatial and directional consistency of the derived subtrees. This framework has been applied to the pulmonary AV classification using a random forest (RF) pre-classifier to exploit the local anatomical differences of arteries and veins. The evaluation of the system was performed using 192 bronchopulmonary segment phantoms, 48 anthropomorphic pulmonary CT phantoms, and 26 lungs from noncontrast CT images with precise voxel-based reference standards obtained by manually labelling the vessel trees. The experiments reveal a relevant improvement in the accuracy ( ∼ 20%) of the vessel particle classification with the proposed framework with respect to using only the pre-classification based on local information applied to the whole area of the lung under study. The results demonstrated the accurate differentiation between arteries and veins in both clinical and synthetic cases, specifically when the image quality can guarantee a good airway segmentation, which opens a huge range of possibilities in the clinical study of cardiopulmonary diseases.
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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.