SynthesisLa Radiologia medica2022
Segmentation techniques of brain arteriovenous malformations for 3D visualization: a systematic review.
Synthesis in La Radiologia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Quantitative MRI in Neuroimaging: A Review of Techniques, Biomarkers, and Emerging Clinical Applications.Brain sciences · 2025Review
- Review
- Feasibility Study for Multimodal Image-Based Assessment of Patient-Specific Intracranial Arteriovenous Malformation Hemodynamics.Journal of clinical medicine · 2025Article
- Artificial Intelligence and Augmented Reality in Vascular Neurosurgery.Acta neurochirurgica. Supplement · 2025Article
- Exploring Augmented Reality Integration in Diagnostic Imaging: Myth or Reality?Diagnostics (Basel, Switzerland) · 2024Review
- Comparative Study of Automated Algorithms for Brain Arteriovenous Malformation Nidus Extent Identification Using 3DRA.Cardiovascular engineering and technology · 2023Article
- Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool.La Radiologia medica · 2023Article
- Imaging Features of Main Hepatic Resections: The Radiologist Challenging.Journal of personalized medicine · 2023Review
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Authors and funding
6 authors.
Funding
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Abstract
backgroundVisualization, analysis and characterization of the angioarchitecture of a brain arteriovenous malformation (bAVM) present crucial steps for understanding and management of these complex lesions. Three-dimensional (3D) segmentation and 3D visualization of bAVMs play hereby a significant role. We performed a systematic review regarding currently available 3D segmentation and visualization techniques for bAVMs.
methodsPubMed, Embase and Google Scholar were searched to identify studies reporting 3D segmentation techniques applied to bAVM characterization. Category of input scan, segmentation (automatic, semiautomatic, manual), time needed for segmentation and 3D visualization techniques were noted.
resultsThirty-three studies were included. Thirteen (39%) used MRI as baseline imaging modality, 9 used DSA (27%), and 7 used CT (21%). Segmentation through automatic algorithms was used in 20 (61%), semiautomatic segmentation in 6 (18%), and manual segmentation in 7 (21%) studies. Median automatic segmentation time was 10 min (IQR 33), semiautomatic 25 min (IQR 73). Manual segmentation time was reported in only one study, with the mean of 5-10 min. Thirty-two (97%) studies used screens to visualize the 3D segmentations outcomes and 1 (3%) study utilized a heads-up display (HUD). Integration with mixed reality was used in 4 studies (12%).
conclusionsA golden standard for 3D visualization of bAVMs does not exist. This review describes a tendency over time to base segmentation on algorithms trained with machine learning. Unsupervised fuzzy-based algorithms thereby stand out as potential preferred strategy. Continued efforts will be necessary to improve algorithms, integrate complete hemodynamic assessment and find innovative tools for tridimensional visualization.
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