ArticleScientific reports2022
Automated segmentation of multiparametric magnetic resonance images for cerebral AVM radiosurgery planning: a deep learning approach.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 4 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence and machine learning driven segmentation and quantification models for brain arteriovenous malformations: A systematic review.Neuroradiology · 2025Pooled it
- Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024Pooled it
- The current landscape of machine learning-based radiomics in arteriovenous malformations: a systematic review and radiomics quality score assessment.Frontiers in neurology · 2024Pooled it
- Segmentation techniques of brain arteriovenous malformations for 3D visualization: a systematic review.La Radiologia medica · 2022Pooled it
- Accelerated Volume-Staged Stereotactic Radiosurgery for Large Cerebral Arteriovenous Malformations: A Single Institutional Experience.Advances in radiation oncology · 2026Article
- Multi-reader evaluation of deep learning-based auto-segmentation of eloquent brain arteriovenous malformation on MRA and white matter tractography in stereotactic radiosurgery.Radiation oncology (London, England) · 2026Article
- Automated Cerebrovascular Segmentation and Visualization of Intracranial Time-of-Flight Magnetic Resonance Angiography Based on Deep Learning.Journal of imaging informatics in medicine · 2025Article
- The new era of artificial intelligence in neuroradiology: current research and promising tools.Arquivos de neuro-psiquiatria · 2024Article
- Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery.Advances in experimental medicine and biology · 2024Review
- Treatment of Brain Arteriovenous Malformations.Advances and technical standards in neurosurgery · 2024Article
- Machine Intelligence in Cerebrovascular and Endovascular Neurosurgery.Advances in experimental medicine and biology · 2024Review
- Article
- Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction.Journal of neuro-oncology · 2023Review
- Deep learning and high-resolution magnetic resonance vascular wall imaging: current challenges and future perspectives.Frontiers in neurologyReview
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9 authors.
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Abstract
Stereotactic radiosurgery planning for cerebral arteriovenous malformations (AVM) is complicated by the variability in appearance of an AVM nidus across different imaging modalities. We developed a deep learning approach to automatically segment cerebrovascular-anatomical maps from multiple high-resolution magnetic resonance imaging/angiography (MRI/MRA) sequences in AVM patients, with the goal of facilitating target delineation. Twenty-three AVM patients who were evaluated for radiosurgery and underwent multi-parametric MRI/MRA were included. A hybrid semi-automated and manual approach was used to label MRI/MRAs with arteries, veins, brain parenchyma, cerebral spinal fluid (CSF), and embolized vessels. Next, these labels were used to train a convolutional neural network to perform this task. Imaging from 17 patients (6362 image slices) was used for training, and 6 patients (1224 slices) for validation. Performance was evaluated by Dice Similarity Coefficient (DSC). Classification performance was good for arteries, veins, brain parenchyma, and CSF, with DSCs of 0.86, 0.91, 0.98, and 0.91, respectively in the validation image set. Performance was lower for embolized vessels, with a DSC of 0.75. This demonstrates the proof of principle that accurate, high-resolution cerebrovascular-anatomical maps can be generated from multiparametric MRI/MRA. Clinical validation of their utility in radiosurgery planning is warranted.
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