ArticleFrontiers in neuroscience2019
A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 1 of them a synthesis that pooled it.
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75 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives.Sovremennye tekhnologii v meditsine · 2021Pooled it
- Sources of Variability in Intracranial Aneurysm Model Reconstruction and Evaluation: A Systematic Investigation.Cardiovascular engineering and technology · 2026Article
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- A high-resolution dataset of mouse brain vasculature for deep learning-based reconstruction.Frontiers in neuroinformatics · 2026Article
- TriSAM: Tri-Plane SAM for Zero-Shot Cortical Blood Vessel Segmentation in VEM Images.IEEE journal of biomedical and health informatics · 2025Article
- Deep Learning Models for Automatic Classification of Anatomic Location in Abdominopelvic Digital Subtraction Angiography.Journal of imaging informatics in medicine · 2025Article
- An advanced skin lesion segmentation and classification framework using deep learning strategies.Scientific reports · 2025Article
- Deep learning-based segmentation of the trigeminal nerve and surrounding vasculature in trigeminal neuralgia.Journal of neurosurgery · 2025Article
- Cherenkov Imaged Bio-Morphological Features Verify Patient Positioning With Deformable Tissue Translocation in Breast Radiation Therapy.Advances in radiation oncology · 2025Article
- Automated Cerebrovascular Segmentation and Visualization of Intracranial Time-of-Flight Magnetic Resonance Angiography Based on Deep Learning.Journal of imaging informatics in medicine · 2025Article
- Three-dimensional, patient-specific simulation of cerebral blood flow velocities as a new approach for individualized stroke prevention and treatment.Computational and structural biotechnology journal · 2025Article
- Beyond visual inspection: the deep learning revolution in quantitative cerebrovascular imaging.Frontiers in neuroscience · 2025Review
- Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney.Scientific reports · 2024Article
- Quantitative modeling of lenticulostriate arteries on 7-T TOF-MRA for cerebral small vessel disease.European radiology experimental · 2024Observational
- Patient-specific cerebral 3D vessel model reconstruction using deep learning.Medical & biological engineering & computing · 2024Article
- Deep Learning for 3D Vascular Segmentation in Phase Contrast Tomography.Research square · 2024Article
- Automated detection and classification of coronary atherosclerotic plaques on coronary CT angiography using deep learning algorithm.Quantitative imaging in medicine and surgery · 2024Article
- An Improved Path-Finding Method for the Tracking of Centerlines of Tortuous Internal Carotid Arteries in MR Angiography.Journal of imaging · 2024Article
- Automated intracranial vessel segmentation of 4D flow MRI data in patients with atherosclerotic stenosis using a convolutional neural network.Frontiers in radiology · 2024Article
15 more citing papers are in PubMed but not listed here.
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11 authors.
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Abstract
Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method-the U-net-is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We performed both quantitative and qualitative analyses. The U-net models yielded high performance for both the full and the reduced architecture: A Dice value of ~0.88, a 95HD of ~47 voxels and an AVD of ~0.4 voxels. The visual analysis revealed excellent performance in large vessels and sufficient performance in small vessels. Pathologies like cortical laminar necrosis and a rete mirabile led to limited segmentation performance in few patients. The U-net outperfomed the traditional graph-cuts method (Dice ~0.76, 95HD ~59, AVD ~1.97). Our work highly encourages the development of clinically applicable segmentation tools based on deep learning. Future works should focus on improved segmentation of small vessels and methodologies to deal with specific pathologies.
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