IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning

Xi Yang, Ding Xia, Taichi Kin, Takeo Igarashi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 2656-2666

Abstract


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/intra2d2019/IntrA.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Yang_2020_CVPR,
author = {Yang, Xi and Xia, Ding and Kin, Taichi and Igarashi, Takeo},
title = {IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}