ArticleNeuroinformatics2023
Towards Automated Brain Aneurysm Detection in TOF-MRA: Open Data, Weak Labels, and Anatomical Knowledge.
Article in Neuroinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Shifting the paradigm in intracranial aneurysm detection with deep learning: A diagnostic accuracy meta-analysis and meta-regression.Neurosurgical review · 2026Pooled it
- Systematic review of artificial intelligence methods for detection and segmentation of unruptured intracranial aneurysms using medical imaging.Medical & biological engineering & computing · 2025Pooled it
- Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: a systematic review.BMC medical imaging · 2024Pooled it
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- The TopCoW Challenge - Topology-Aware Circle of Willis Segmentation for CT and MR Angiography.NEJM AI · 2026Article
- VeNet: a lightweight neural network for efficient brain vessel segmentation in endovascular robotic surgery.Scientific reports · 2026Article
- A Landmark-Guided Dual-Stream Synergistic Framework for Automated Intracranial Aneurysm Detection in Magnetic Resonance Angiography.Journal of imaging informatics in medicine · 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
- Article
- A Mask R-CNN-Based Approach for Brain Aneurysm Detection and Segmentation from TOF-MRA Data.Brain sciences · 2025Article
- Article
- Intracranial aneurysm segmentation with nnU-net: utilizing loss functions and automated vessel extraction.Vessel plus · 2025Article
- Intracranial aneurysm CTA images and 3D models dataset with clinical morphological and hemodynamic data.Scientific data · 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
- Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA.Neuroinformatics · 2024Article
- Time-of-Flight MRA of Intracranial Aneurysms with Interval Surveillance, Clinical Segmentation and Annotations.Scientific data · 2024Article
- A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research.Journal of magnetic resonance imaging : JMRI · 2024Review
- DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images.Visual computing for industry, biomedicine, and art · 2022Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Brain aneurysm detection in Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) has undergone drastic improvements with the advent of Deep Learning (DL). However, performances of supervised DL models heavily rely on the quantity of labeled samples, which are extremely costly to obtain. Here, we present a DL model for aneurysm detection that overcomes the issue with "weak" labels: oversized annotations which are considerably faster to create. Our weak labels resulted to be four times faster to generate than their voxel-wise counterparts. In addition, our model leverages prior anatomical knowledge by focusing only on plausible locations for aneurysm occurrence. We first train and evaluate our model through cross-validation on an in-house TOF-MRA dataset comprising 284 subjects (170 females / 127 healthy controls / 157 patients with 198 aneurysms). On this dataset, our best model achieved a sensitivity of 83%, with False Positive (FP) rate of 0.8 per patient. To assess model generalizability, we then participated in a challenge for aneurysm detection with TOF-MRA data (93 patients, 20 controls, 125 aneurysms). On the public challenge, sensitivity was 68% (FP rate = 2.5), ranking 4th/18 on the open leaderboard. We found no significant difference in sensitivity between aneurysm risk-of-rupture groups (p = 0.75), locations (p = 0.72), or sizes (p = 0.15). Data, code and model weights are released under permissive licenses. We demonstrate that weak labels and anatomical knowledge can alleviate the necessity for prohibitively expensive voxel-wise annotations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.