ArticleScientific reports2023
Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: a systematic review.BMC medical imaging · 2024Pooled it
- Clinical Comparison of Two Versions of a Commercial Artificial Intelligence System for Identifying Candidate Locations of Cerebral Aneurysms: Reduction and Characterization of False-Positive Findings.Journal of clinical medicine · 2026Article
- Artificial Intelligence for Intracranial Aneurysm Detection, Rupture-Risk Prediction, and Prognosis Evaluation: Recent Advances and Clinical Integration.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- Application of deep learning strategies in the standardization and diagnostic efficiency enhancement of chest X-ray imaging.BMC medical imaging · 2026Article
- Anatomically-guided masked autoencoder pre-training for aneurysm detection.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2026Article
- Cerebrovascular morphology: Insights into normal variations, aging effects and disease implications.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2025Review
- Methodological Challenges in Deep Learning-Based Detection of Intracranial Aneurysms: A Scoping Review.Neurointervention · 2025Review
- Imaging of Intracranial Aneurysms: A Review of Standard and Advanced Imaging Techniques.Translational stroke research · 2025Review
- Accuracy of an nnUNet Neural Network for the Automatic Segmentation of Intracranial Aneurysms, Their Parent Vessels, and Major Cerebral Arteries from MRI-TOF.AJNR. American journal of neuroradiology · 2025Article
- Applications of deep learning in intracranial aneurysm imaging: A scoping review of detection, risk prediction, and emerging prognostic models.Current journal of neurology · 2025Review
- Intracranial aneurysm segmentation with nnU-net: utilizing loss functions and automated vessel extraction.Vessel plus · 2025Article
- Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA.Neuroinformatics · 2024Article
- Vessel-aware aneurysm detection using multi-scale deformable 3D attention.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Automated Method for Intracranial Aneurysm Classification Using Deep Learning.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and reliable detection of intracranial aneurysms is vital for subsequent treatment to prevent bleeding. However, the detection of intracranial aneurysms can be time-consuming and even challenging, and there is great variability among experts, especially in the case of small aneurysms. This study aimed to detect intracranial aneurysms accurately using a convolutional neural network (CNN) with 3D time-of-flight magnetic resonance angiography (TOF-MRA). A total of 154 3D TOF-MRA datasets with intracranial aneurysms were acquired, and the gold standards were manually drawn by neuroradiologists. We also obtained 113 subjects from a public dataset for external validation. These angiograms were pre-processed by using skull-stripping, signal intensity normalization, and N4 bias correction. The 3D patches along the vessel skeleton from MRA were extracted. Values of the ratio between the aneurysmal and the normal patches ranged from 1:1 to 1:5. The semantic segmentation on intracranial aneurysms was trained using a 3D U-Net with an auxiliary classifier to overcome the imbalance in patches. The proposed method achieved an accuracy of 0.910 in internal validation and external validation accuracy of 0.883 with a 2:1 ratio of normal to aneurysmal patches. This multi-task learning method showed that the aneurysm segmentation performance was sufficient to be helpful in an actual clinical setting.
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