ArticleScientific reports2017
Robust Segmentation of the Full Cerebral Vasculature in 4D CT of Suspected Stroke Patients.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis.Health technology assessment (Winchester, England) · 2024Pooled it
- Computational Fluid Dynamics Analysis of Cerebrovascular Hemodynamic Differences in Adults with Sickle Cell Disease: Comparing Stroke and Non-Stroke Cohorts.Annals of biomedical engineering · 2026Article
- A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging.Scientific data · 2023Article
- Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- Numerical Assessment of the Risk of Abnormal Endothelialization for Diverter Devices: Clinical Data Driven Numerical Study.Journal of personalized medicine · 2022Article
- A Minimally Interactive Method for Labeling Respiratory Phases in Free-Breathing Thoracic Dynamic MRI for Constructing 4D Images.IEEE transactions on bio-medical engineering · 2022Article
- An evaluation of performance measures for arterial brain vessel segmentation.BMC medical imaging · 2021Article
- Article
- Automatic segmentation, feature extraction and comparison of healthy and stroke cerebral vasculature.NeuroImage. Clinical · 2021Article
- Automatic collateral circulation scoring in ischemic stroke using 4D CT angiography with low-rank and sparse matrix decomposition.International journal of computer assisted radiology and surgery · 2020Article
- Cerebral Artery and Vein Segmentation in Four-dimensional CT Angiography Using Convolutional Neural Networks.Radiology. Artificial intelligence · 2020Article
- Multiscale modeling of human cerebrovasculature: A hybrid approach using image-based geometry and a mathematical algorithm.PLoS computational biology · 2020Article
- BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease.Frontiers in artificial intelligence · 2020Article
- Deep Learning for Automated Delineation of Pediatric Cerebral Arteries on Pre-operative Brain Magnetic Resonance Imaging.Frontiers in surgery · 2020Article
- Color-Mapping of 4D-CTA for the Detection of Cranial Arteriovenous Shunts.AJNR. American journal of neuroradiology · 2019Article
- Semi-automated 3D segmentation of human skeletal muscle using Focused Ion Beam-Scanning Electron Microscopic images.Journal of structural biology · 2019Article
- A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease.Frontiers in neuroscience · 2019Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A robust method is presented for the segmentation of the full cerebral vasculature in 4-dimensional (4D) computed tomography (CT). The method consists of candidate vessel selection, feature extraction, random forest classification and postprocessing. Image features include among others the weighted temporal variance image and parameters, including entropy, of an intensity histogram in a local region at different scales. These histogram parameters revealed to be a strong feature in the detection of vessels regardless of shape and size. The method was trained and tested on a large database of 264 patients with suspicion of acute ischemia who underwent 4D CT in our hospital in the period January 2014 to December 2015. Five subvolumes representing different regions of the cerebral vasculature were annotated in each image in the training set by medical assistants. The evaluation was done on 242 patients. A total of 16 (<8%) patients showed severe under or over segmentation and were reported as failures. One out of five subvolumes was randomly annotated in 159 patients and was used for quantitative evaluation. Quantitative evaluation showed a Dice coefficient of 0.91 ± 0.07 and a modified Hausdorff distance of 0.23 ± 0.22 mm. Therefore, robust vessel segmentation in 4D CT is feasible with good accuracy.
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