发表机构
Uppsala University; Cornell University; Cornell Tech; Weill Cornell Medicine; Radiological Society of North America; Taipei Medical University Hospital; UC San Diego; Prince Sultan Military Medical City; Liverpool Hospital; Clinical Center of the University of Sarajevo; Memorial University of Newfoundland; Fleni; University of California San Francisco; Koç University School of Medicine; Intermed IUHW Hospital; Hacettepe University; Duke University; China Medical University Hospital（乌普萨拉大学; 康奈尔大学; 康奈尔科技学院; 威尔康奈尔医学院; 北美放射学会; 台北医学大学附属医院; 加州大学圣地亚哥分校; 苏丹王子军事医疗城; 利物浦医院; 萨拉热窝大学临床中心; 纽芬兰纪念大学; 弗莱尼医院; 加州大学旧金山分校; 科奇大学医学院; 国际医疗福祉大学医院; 哈塞特佩大学; 杜克大学; 中国医药大学附属医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究构建了RSNA-ICA数据集，包含7202个CTA、MRA和MRI序列，用于开发和评估颅内动脉瘤检测的AI算法，并提供专家标注和三维分割，以推进动脉瘤检测技术。
AI 中文摘要
颅内动脉瘤破裂与显著的发病率和死亡率相关，然而动脉瘤的检测仍然具有挑战性，尤其是对于小病灶以及在常规非血管造影影像检查中。为了支持颅内动脉瘤检测和定位的人工智能（AI）算法的开发与评估，北美放射学会（RSNA）与美国神经放射学会（ASNR）、神经介入外科学会（SNIS）以及欧洲神经放射学会（ESNR）合作，整理了RSNA颅内动脉瘤（RSNA-ICA）数据集。该数据集为2025年RSNA颅内动脉瘤检测挑战赛而开发，是一个大型、公开可用、专家标注的数据集，包含来自5大洲12个国家21家机构收集的4278名成年患者的7202个CTA、MRA和MRI序列。该数据集包括来自患有和未患有颅内囊状动脉瘤的患者的2566个CTA、2166个MRA和2470个MRI序列，提供了显著的地理和影像多样性。专家标注同时指示动脉瘤的存在和位置，此外，178个序列还包含挑战定义的血管位置的三维分割。RSNA-ICA被用于2025年RSNA颅内动脉瘤检测挑战赛中算法的开发和评估。在7202个影像序列中，5041个通过MIRA（此https URL）公开可用，而其余序列用于挑战赛的公开和私有测试集。该数据集免费提供给研究界用于非商业用途，并为推进基于AI的动脉瘤检测（涵盖血管造影和常规神经影像检查）提供了全面资源。
英文摘要
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, 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.
CommentsDataset available via MIRA: https://mira.rsna.org/dataset/7
