IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning
This dataset addresses the need for 3D medical data in diagnosing intracranial aneurysms and supporting surgical planning, though it is incremental as it extends existing 2D approaches to 3D.
The authors introduced IntrA, an open-access 3D intracranial aneurysm dataset, enabling the application of points-based and mesh-based deep learning models for classification and segmentation tasks, and provided a benchmark by testing state-of-the-art networks.
Medicine is an important application area for deep learning models. Research in this field is a combination of medical expertise and data science knowledge. In this paper, instead of 2D medical images, we introduce an open-access 3D intracranial aneurysm dataset, IntrA, that makes the application of points-based and mesh-based classification and segmentation models available. Our dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction. We provide a large-scale benchmark of classification and part segmentation by testing state-of-the-art networks. We also discuss the performance of each method and demonstrate the challenges of our dataset. The published dataset can be accessed here: https://github.com/intra3d2019/IntrA.