arXiv Computer Vision
The RSNA Intracranial Aneurysm (RSNA-ICA) Dataset
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arXiv AI
Jul 8
Shape Over Intensity: Directional Topological Encoding for False Positive Reduction in Intracranial Aneurysm Detection
arXiv:2607. 05317v2 Announce Type: replace-cross Abstract: Automated detection of intracranial aneurysms (IAs) from CT angiography (CTA) is severely hindered by high false-positive rates.
arXiv Computer Vision
Aug 26
Intracranial Aneurysm Classification and Segmentation via Tri-Axial ROI and Multi-Task Learning
arXiv:2606.26706v2 Announce Type: replace Abstract: Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on...
arXiv AI
Jul 7
Topological Shape Representation for Aneurysm -- Bifurcation Detection
arXiv:2607. 05317v1 Announce Type: cross Abstract: Automated detection of intracranial aneurysms (IAs) from CT angiography (CTA) is severely hindered by high false-positive rates.
arXiv Machine Learning
Jul 15
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
arXiv:2312. 17670v5 Announce Type: replace-cross Abstract: The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain.
arXiv Computer Vision
Sep 16
Automated Distinction of Intimal and Medial Intracranial Arterial Calcification from CT Head
The study evaluates three automated methods for distinguishing intimal from medial intracranial arterial calcifications (IACs) on non‑contrast head CT scans. Using segmentation masks, the methods—an adapted visual score, a sphericity metric, and shape embeddings from a medical foundation model—achieved comparable performance, with the embedding approach achieving the highest weighted F1 scores (71.5% for single arteries, 59.8% for joint classification). The approach remains robust when using automated versus manual segmentation masks, demonstrating feasibility for fully automated IAC subtype quantification.
arXiv AI
Sep 16
A Vision-Language Foundation Model for Precise and Comprehensive Brain Tumor Diagnosis from Preoperative Multimodal Data
arXiv:2609.16597v1 Announce Type: cross Abstract: Background Non-invasive presurgical diagnosis of brain tumor types from Magnetic Resonance Imaging (MRI) is essential but challenging due to overlapp...