ArticleRadiology. Artificial intelligence2020
Cerebral Artery and Vein Segmentation in Four-dimensional CT Angiography Using Convolutional Neural Networks.
Article in Radiology. Artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Automated Craniofacial Artery Segmentation with Vessel Enhancement-Guided Deep Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Differentiating effective salvage from ineffective delayed compensation in anterior circulation stroke: a dynamic quantitative collateral index.Frontiers in medicine · 2026Article
- Future Applications of Cardiothoracic CT.Radiology · 2025Review
- Focused view CT angiography for selective visualization of stroke related arteries: technical feasibility.European radiology · 2023Article
- Atypical presentation of COVID-19 in older patients is associated with frailty but not with adverse outcomes.European geriatric medicine · 2023Article
- Effects of Pulsatile Flow Rate and Shunt Ratio in Bifurcated Distal Arteries on Hemodynamic Characteristics Involved in Two Patient-Specific Internal Carotid Artery Sidewall Aneurysms: A Numerical Study.Bioengineering (Basel, Switzerland) · 2022Article
- BV-GAN: 3D time-of-flight magnetic resonance angiography cerebrovascular vessel segmentation using adversarial CNNs.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Automated segmentation of multiparametric magnetic resonance images for cerebral AVM radiosurgery planning: a deep learning approach.Scientific reports · 2022Article
- Myotonic dystrophy type 1 as a major risk factor for severe COVID-19?Acta neurologica Belgica · 2021Observational
- CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias.La Radiologia medica · 2021Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo implement and test a deep learning approach for the segmentation of the arterial and venous cerebral vasculature with four-dimensional (4D) CT angiography. MATERIALS AND
methodsPatients who had undergone 4D CT angiography for the suspicion of acute ischemic stroke were retrospectively identified. A total of 390 patients evaluated in 2014 (
resultsThe average time for processing arterial and venous cerebral vasculature with the network was less than 90 seconds. The mean Dice similarity coefficient in the test set was 0.80 ± 0.04 (standard deviation) for the arteries and 0.88 ± 0.03 for the veins. The mean relative absolute volume difference was 7.3% ± 5.7 for the arteries and 8.5% ± 4.8 for the veins. Most of the segmentations (
conclusionThe proposed convolutional neural network enables accurate artery and vein segmentation with 4D CT angiography with a processing time of less than 90 seconds.© RSNA, 2020.
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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.