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:
Xi Yang, Ding Xia, Taichi Kin, Takeo Igarashi