Authors: Maria Correia de Verdier, Rachit Saluja, Jason Sho, Maryam Vabarizad, Rennie Yung-Chieh Chen, Uyen N. T. Nguyen, Mona Alrehaili, Layal Aweidah, +32 more
Organizations: Department of Surgical Sciences, Section of Neuroradiology, Uppsala University, Uppsala, Sweden · Department of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA · Department of Radiology, Weill Cornell Medicine, New York, NY, USA · Informatics Department, Radiological Society of North America, Oak Brook, IL, USA · Taipei Medical University Hospital, Taipei, Taiwan · Department of Radiology, UC San Diego, San Diego, CA, USA · Department of Radiology, Prince Sultan Military Medical City, Riyadh, Saudi Arabia · Interventional Radiology, Liverpool Hospital, Sydney, Australia · Clinical Center of the University of Sarajevo, Sarajevo, Bosnia and Herzegovina · Health Sciences Centre, Memorial University of Newfoundland, Canada · Departamento de Diagnóstico por Imágenes, Fleni, Buenos Aires, Argentina · Center for Intelligent Imaging, Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA · Koç University School of Medicine, Istanbul, Turkey · Department of Radiology, Intermed IUHW Hospital, International Medical Center, Ulaanbaatar, Mongolia · Department of Radiology, Hacettepe University, Faculty of Medicine, Ankara, Turkey · Department of Radiology, Duke University, Durham, NC, USA · Big Data Center, China Medical University Hospital, Taichung, Taiwan · Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan · Department of Biomedical Informatics, School of Medicine, China Medical University, Taichung, Taiwan · Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan · Office of Research and Development, Asia University, Taichung, Taiwan · Aga Khan University Hospital, Pakistan · University of Health Sciences, Ümraniye Training and Research Hospital, Istanbul, Turkey · Department of Radiology, Philippine General Hospital, University of the Philippines Manila, Manila, Philippines · Department of Radiology, Kingston Health Sciences Centre, Kingston, ON, Canada · Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain · University Hospitals Cleveland Medical Center, Cleveland, OH, USA · Gold Coast Hospital and Health Service, Queensland, Australia · Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand · Department of Radiology & Biomedical Imaging; Department of Medicine, Division of Clinical Informatics and Digital Transformation, University of California San Francisco, San Francisco, CA, USA · Department of Radiology, Division of Neuroradiology/ENT, Thomas Jefferson University, Philadelphia, PA, USA · The Jackson Laboratory, Bar Harbor, ME, USA · Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, USA · University of California Irvine, Irvine, CA, USA · Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil · Department of Medical Imaging, University of Toronto, Toronto, Canada · Department of Radiology, Ohio State University, Columbus, OH, USA · Department of Radiology and Imaging sciences, University of Utah School of medicine, Salt Lake City, UT, USA · Department of Radiology, University of California San Diego, San Diego, CA, USA · Department of Radiology, Scripps Clinic Medical Group, San Diego, CA, USA
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.
Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on both aneurysm morphology and anatomical location, yet existing automated methods remain limited to binary detection without fine-grained anatomical classification or multi-class segmentation. We present a multi-task framework that simultaneously performs multi-label classification, multi-class aneurysm segmentation, and multi-class vessel segmentation across 13 anatomical locations and four imaging modalities (CTA, MRA, T2, T1-post). Our two-stage approach combines a fast 2D tri-axial Region of Interest (ROI) extraction method with a 3D multi-task nnU-Net backbone. A dual-decoder design mitigates the extreme volume imbalance between aneurysm and vessel classes, while cross-attention pooling and modality-specific auxiliary heads improve feature learning across heterogeneous inputs. Our two-fold ensemble achieved 2nd place in the RSNA 2025 Intracranial Aneurysm Detection challenge. Code, model weights, and a 3D Slicer plugin are publicly available.
Pengcheng Shi, Kaiyuan Yang, Houjing Huang +6
Medical Image Insights Co. Ltd., Shanghai, China · University of Zurich, Zurich, Switzerland
Automated detection of intracranial aneurysms (IAs) from CT angiography (CTA) is severely hindered by high false-positive rates. Convolutional neural networks (CNNs) rely on local pixel intensities, causing systematic confusion between saccular aneurysms and vascular bifurcations - a problem especially acute for small lesions (<3 mm), where detection sensitivity falls below 60%. We propose a plug-and-play, topology-aware false-positive reduction framework evaluating the Smooth Euler Characteristic Transform (SECT) - a directional representation encoding global 3D vascular geometry independently of intensity - against persistence-based summaries (Persistence Images and Landscapes), tested on a stratified subset of the RSNA 2025 dataset. SECT achieves an AUC of 0.943, substantially outperforming direction-agnostic methods (AUC ~0.68), and exhibits a clinical performance inversion: it excels on the sub-3 mm cohort, maintaining 0.943 AUC and 78.5% sensitivity at 95% specificity. The representation is also scanner-agnostic, achieving 0.927 mean AUC under leave-one-scanner-out (LOGO) validation across four manufacturers. By capturing asymmetric geometric invariants rather than intensity profiles, SECT reliably resolves the primary structural confounder in IA detection, positioning it as a robust downstream filter for hybrid deep-learning diagnostic pipelines.
Akshay Gokhale, Mansi Dhamne
Computer Science and Engineering Sardar Patel Institute of Technology Mumbai, India · Computer Engineering Sardar Patel Institute of Technology Mumbai, India
Intracranial aneurysms (IAs), characterized by unpredictable growth and risk of rupture, are a major cause of stroke and can lead to life-threatening hemorrhages with high mortality and long-term disability. With aging populations, the incidence and overall burden of cerebrovascular diseases are expected to increase, highlighting the need for scalable approaches to analyze complex medical data and improve population-level understanding of these conditions. While digital twins and deep learning offer promising avenues for improving diagnosis, prognosis, and treatment, their effectiveness is limited by the scarcity of large-scale, high-quality medical data and corresponding labels. We present Synthetic VAsculature (SynVA), a modular toolkit for vascular mesh generation and anatomically consistent aneurysm synthesis. SynVA combines novel flow-matching-based methods for generating healthy vessel meshes with learning-based approaches for anatomy-conditioned aneurysm mesh generation - aneurysms are computed from pre-existing vascular geometries rather than being generated in isolation. In addition, we introduce the SynVA procedural model for vascular and aneurysm synthesis based solely on physiological principles and statistical priors, which enables the generation of large-scale datasets (e.g., for the training of mesh-based generative models). To this end, we release a dataset of 50,000 fully labeled mesh samples for a variety of downstream vision tasks, such as semantic segmentation. Extensive quantitative and qualitative evaluations demonstrate that SynVA generates realistic vessel geometries and anatomically plausible aneurysms. Specifically, our experiments indicate that some methods produce aneurysm shapes more aligned with expert human perception while others perform better on quantitative similarity metrics with reconstructions of real aneurysms.
Marten J. Finck, Niklas C. Koser, Sarker M. Mahfuz +7
Visual Computing and Artificial Intelligence, Kiel University, Germany · Institute for Medical Informatics and Statistics, Kiel University, Germany · Clinic for Neuroradiology, Medical Faculty, Magdeburg University, Germany +2