Rupture of Intracranial Aneurysms (IAs) is the leading cause of non-traumatic intracranial hemorrhage. Early detection of aneurysms prior to rupture or their prompt identification in cases of intracranial hemorrhage is critical and guides treatment strategies. The development of artificial intelligence tools to automate the labor-intensive detection and analysis of IAs is an active research field, but it depends on the availability of large, well-curated datasets for robust model training, validation, and testing. Collaborative data sharing is essential for advancing this field, yet remains relatively uncommon. Here, we present a collection of 172 Computed Tomography Angiography (CTA) scan series—a widely available and commonly used modality for the diagnosis of IAs—supplemented with structured metadata. The dataset comprises 90 scans from healthy patients and 82 scans from patients with IAs of diverse shapes, sizes, and anatomical locations, annotated and validated by two experts. The annotations include 122 surface mesh models in STL format. This openly accessible dataset is intended to support the development of automated segmentation or classification tools, medical image analysis, and assessment of disease progression risks through morphometric and hemodynamic evaluations.
Blagosklonova et al. (Fri,) studied this question.