ArticleIEEE transactions on bio-medical engineering2019
Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks.
Article in IEEE transactions on bio-medical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Imaging endpoints of intracranial atherosclerosis using vessel wall MR imaging: a systematic review.Neuroradiology · 2021Pooled it
- VesMamba: Vessel Morphology-Enhanced State Space Model for Cerebrovascular Delineation.NMR in biomedicine · 2026Article
- Deep learning for high-resolution magnetic resonance vessel wall imaging: image reconstruction, stenosis diagnosis and plaque calculation.European radiology · 2026Article
- Clinically oriented deep learning framework for automated vessel wall segmentation in black-blood MRI: a multi-center study.European radiology · 2026Article
- Rapid vessel segmentation and reconstruction of head and neck angiograms from MR vessel wall images.NPJ digital medicine · 2025Article
- Self-supervised learning for accurately modelling hierarchical evolutionary patterns of cerebrovasculature.Nature communications · 2024Article
- Efficient and Accurate 3D Thickness Measurement in Vessel Wall Imaging: Overcoming Limitations of 2D Approaches Using the Laplacian Method.Journal of cardiovascular development and disease · 2024Article
- A 2.5D Self-Training Strategy for Carotid Artery Segmentation in T1-Weighted Brain Magnetic Resonance Images.Journal of imaging · 2024Article
- Impact of lesion size on reproducibility of quantitative measurement and radiomic features in vessel wall MRI.European radiology · 2023Article
- VWI-APP: Vessel wall imaging-dedicated automated processing pipeline for intracranial atherosclerotic plaque quantification.Medical physics · 2023Article
- Intracranial vessel wall segmentation with deep learning using a novel tiered loss function incorporating class inclusion.Medical physics · 2022Article
- Automated morphologic analysis of intracranial and extracranial arteries using convolutional neural networks.The British journal of radiology · 2022Article
- MRA-free intracranial vessel localization on MR vessel wall images.Scientific reports · 2022Article
- Disparate trends of atherosclerotic plaque evolution in stroke patients under 18-month follow-up: a 3D whole-brain magnetic resonance vessel wall imaging study.The neuroradiology journal · 2022Article
- Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images.Frontiers in neuroscience · 2022Article
- Current Clinical Applications of Intracranial Vessel Wall MR Imaging.Seminars in ultrasound, CT, and MR · 2021Review
- Neural network enhanced 3D turbo spin echo for MR intracranial vessel wall imaging.Magnetic resonance imaging · 2021Article
- INTRACRANIAL VESSEL WALL SEGMENTATION FOR ATHEROSCLEROTIC PLAQUE QUANTIFICATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2021Article
- Intracranial Atherosclerotic Plaque Characteristics and Burden Associated With Recurrent Acute Stroke: A 3D Quantitative Vessel Wall MRI Study.Frontiers in aging neuroscience · 2021Article
- Fully automated and robust analysis technique for popliteal artery vessel wall evaluation (FRAPPE) using neural network models from standardized knee MRI.Magnetic resonance in medicine · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectiveTo develop an automated vessel wall segmentation method using convolutional neural networks to facilitate the quantification on magnetic resonance (MR) vessel wall images of patients with intracranial atherosclerotic disease (ICAD).
methodsVessel wall images of 56 subjects were acquired with our recently developed whole-brain three-dimensional (3-D) MR vessel wall imaging (VWI) technique. An intracranial vessel analysis (IVA) framework was presented to extract, straighten, and resample the interested vessel segment into 2-D slices. A U-net-like fully convolutional networks (FCN) method was proposed for automated vessel wall segmentation by hierarchical extraction of low- and high-order convolutional features.
resultsThe network was trained and validated on 1160 slices and tested on 545 slices. The proposed segmentation method demonstrated satisfactory agreement with manual segmentations with Dice coefficient of 0.89 for the lumen and 0.77 for the vessel wall. The method was further applied to a clinical study of additional 12 symptomatic and 12 asymptomatic patients with >50% ICAD stenosis at the middle cerebral artery (MCA). Normalized wall index at the focal MCA ICAD lesions was found significantly larger in symptomatic patients compared to asymptomatic patients.
conclusionWe have presented an automated vessel wall segmentation method based on FCN as well as the IVA framework for 3-D intracranial MR VWI. SIGNIFICANCE: This approach would make large-scale quantitative plaque analysis more realistic and promote the adoption of MR VWI in ICAD management.
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