AAA-100: A Curated Dataset of 3D Watertight Abdominal Aortic Aneurysm Models

An abdominal aortic aneurysm (AAA) is a local dilatation of the abdominal aorta exceeding 30 mm that might rupture, with fatal outcomes in 70-80% of cases. Personalized 3D models of AAAs, including surrounding vasculature such as iliac and renal arteries play an important role in tailored clinical decision-making for AAA patients. Models could be used for, e.g., AAA growth modeling, stentgraft sizing and positioning for endovascular aorta repair (EVAR) procedures, or 3D printing for surgical practice. Extracting high-quality 3D arterial models from imaging modalities such as computed tomography angiography (CTA) is a time-consuming and challenging problem. For downstream applications such as computational fluid dynamics (CFD) or shape analysis, models should have sub-voxel accuracy, be watertight, and adhere to topological constraints. We present the AAA-100 dataset, containing 100 detailed 3D AAA models with consistent anatomical boundaries acquired semi-automatically from pre-operative CTA scans. These models span a wide range of possible AAA pathology. Moreover, all models are carefully curated to be anatomically and topologically correct. A detailed description of the data set and file structure is provided in description.pdf. We kindly ask you to cite the following works when using the AAA-100 dataset in your research Alblas, D., Suk, J., Brune, C., Yeung, K. K., & Wolterink, J. M. (2025). SIRE: Scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks. Medical Image Analysis, 103467. Rygiel, P., Alblas, D., Brune, C., Yeung, K. K., & Wolterink, J. M. (2024). Global Control for Local SO (3)-Equivariant Scale-Invariant Vessel Segmentation. arXiv preprint arXiv:2403.15314.腹主动脉瘤（abdominal aortic aneurysm, AAA）是指腹主动脉局部扩张直径超过30mm，存在破裂风险，且70%~80%的破裂病例会导致致命后果。针对腹主动脉瘤患者的个性化临床决策，包含髂动脉、肾动脉等周围脉管系统的腹主动脉瘤个性化三维模型发挥着关键作用。此类模型可应用于诸多场景，例如腹主动脉瘤生长建模、腔内主动脉修复（endovascular aorta repair, EVAR）术中支架移植物的尺寸选型与位置规划，或是用于手术实操练习的3D打印等。 从计算机断层血管造影（computed tomography angiography, CTA）等成像模态中提取高质量的三维动脉模型，是一项耗时且极具挑战性的工作。对于计算流体动力学（computational fluid dynamics, CFD）或形状分析等下游应用而言，所提取的模型需具备亚体素精度、水密性，且符合拓扑约束条件。 本研究发布AAA-100数据集，包含100个精细的腹主动脉瘤三维模型。这些模型通过半自动方式从术前CTA扫描中获取，具备统一的解剖学边界，涵盖了多种不同类型的腹主动脉瘤病理特征。此外，所有模型均经过严格审核，确保其解剖学与拓扑学正确性。 数据集与文件结构的详细说明可参见"description.pdf"。 若您在研究中使用AAA-100数据集，请引用以下文献： Alblas, D., Suk, J., Brune, C., Yeung, K. K., & Wolterink, J. M. (2025). SIRE: Scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks. 《医学图像分析》, 103467. Rygiel, P., Alblas, D., Brune, C., Yeung, K. K., & Wolterink, J. M. (2024). Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation. arXiv预印本 arXiv:2403.15314.