ArticleNature communications2020
Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed, 143 citations in OpenAlex.
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- Deep learning for high-resolution magnetic resonance vessel wall imaging: image reconstruction, stenosis diagnosis and plaque calculation.European radiology · 2026Article
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- Percutaneous nephrostomy guidance by a convolutional-neural-network-based optical coherence tomography endoscope.Communications engineering · 2026Article
- Deep learning for fast screening and localization of spinal dural arteriovenous fistulas to enhance clinical workflow.NPJ digital medicine · 2026Article
- Progress on using CTA-based vessel distance index to predict futile recanalization: a retrospective observational study.BMC neurology · 2026Observational
- Application and validation of AI-assisted 3D-Printed gastroduodenal anatomical variation models in specialized nursing training.Frontiers in bioengineering and biotechnology · 2026Article
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- Assessing an Automated Noncontrast CT-based Pipeline for Sacral Tumor Classification Using a Hip Bone Reference Frame.Radiology. Imaging cancer · 2026Article
- Semi-automatic annotation of brain vessels in magnetic resonance angiography images.Scientific data · 2025Article
- Automatic segmentation and reconstruction of lower-extremity arteries from computed tomography angiography images via a deep learning framework.Quantitative imaging in medicine and surgery · 2025Article
- Anatomy of Inferior Temporal Arteries in Relation to Middle Cranial Fossa Structures: A Postmortem Computed Tomography Angiography Study.Journal of neurological surgery. Part B, Skull base · 2025Article
- The impact of post-processing images of abdominal CT small vessels using contrast enhancement boost technique: a retrospective study.Quantitative imaging in medicine and surgery · 2025Article
- Rapid vessel segmentation and reconstruction of head and neck angiograms from MR vessel wall images.NPJ digital medicine · 2025Article
- Risk prediction for elderly cognitive impairment by radiomic and morphological quantification analysis based on a cerebral MRA imaging cohort.European radiology · 2025Article
- Outer lumen arterial imaging with CTA: A potential tool to diagnose vertebral artery dissection.Radiology case reports · 2025Article
- AI-Based Automated Quantification of Arterial Stenosis in Head and Neck CT Angiography: A Comparison with Manual Measurements from Digital Subtraction Angiography and CT Angiography.Clinical neuroradiology · 2025Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
15 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The computed tomography angiography (CTA) postprocessing manually recognized by technologists is extremely labor intensive and error prone. We propose an artificial intelligence reconstruction system supported by an optimized physiological anatomical-based 3D convolutional neural network that can automatically achieve CTA reconstruction in healthcare services. This system is trained and tested with 18,766 head and neck CTA scans from 5 tertiary hospitals in China collected between June 2017 and November 2018. The overall reconstruction accuracy of the independent testing dataset is 0.931. It is clinically applicable due to its consistency with manually processed images, which achieves a qualification rate of 92.1%. This system reduces the time consumed from 14.22 ± 3.64 min to 4.94 ± 0.36 min, the number of clicks from 115.87 ± 25.9 to 4 and the labor force from 3 to 1 technologist after five months application. Thus, the system facilitates clinical workflows and provides an opportunity for clinical technologists to improve humanistic patient care.
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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.