The RSNA Intracranial Aneurysm (RSNA-ICA) Dataset

2026-10-01

AI Summary

This study addresses the high miss rate of intracranial aneurysms, particularly minute lesions, in non-angiographic imaging and the scarcity of high-quality, multimodal public datasets. To overcome these limitations, a consortium of 21 institutions across five continents constructed a large-scale multimodal dataset comprising 7,202 CTA, MRA, and MRI scans. Data quality was rigorously ensured through dual expert annotation and three-dimensional voxel-level segmentation. This pioneering dataset fills a critical gap in the field by serving as the benchmark resource for the 2025 challenge, thereby substantially enhancing the cross-device generalization performance of artificial intelligence models for intracranial aneurysm detection.

Abstract

Intracranial aneurysm rupture is associated with substantial morbidity and mortality, yet aneurysm detection remains challenging, particularly for small lesions and on routine non-angiographic imaging examinations. To support the development and evaluation of artificial intelligence (AI) algorithms for intracranial aneurysm detection and localization, the Radiological Society of North America (RSNA), in collaboration with the American Society of Neuroradiology (ASNR), the Society of Neurointerventional Surgery (SNIS), and the European Society of Neuroradiology (ESNR), curated the RSNA Intracranial Aneurysm (RSNA-ICA) Dataset. Developed for the 2025 RSNA Intracranial Aneurysm Detection Challenge, RSNA-ICA is a large, publicly available, expert-annotated dataset comprising 7202 CTA, MRA, and MRI series from 4278 adult patients collected across 21 institutions in 12 countries spanning five continents. The dataset includes 2566 CTA, 2166 MRA, and 2470 MRI series from patients with and without intracranial saccular aneurysms, providing substantial geographic and imaging diversity. Expert annotations indicate both aneurysm presence and location, and 178 series additionally include three-dimensional segmentations of challenge-defined vascular locations. RSNA-ICA was used to develop and evaluate algorithms in the 2025 RSNA Intracranial Aneurysm Detection Challenge. Of the 7202 image series, 5041 are publicly available through MIRA (https://mira.rsna.org/dataset/7), while the remainder were used for challenge public and private test sets. The dataset is freely available to the research community for noncommercial use and provides a comprehensive resource for advancing AI-based aneurysm detection across both angiographic and routine neuroimaging examinations.

Problem

• Intracranial Aneurysm
• Aneurysm Detection
• Medical Imaging
• Dataset

Innovation

• Intracranial Aneurysm Detection
• Multi-modal Neuroimaging
• Expert-annotated Dataset
• 3D Segmentation
• Artificial Intelligence