IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning

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...

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Bibliographic Details
Main Authors: Yang, Xi, Xia, Ding, Kin, Taichi, Igarashi, Takeo
Format: Conference Proceeding
Language:English
Subjects:
Aneurysm
Biomedical imaging
Brain modeling
Image segmentation
Shape
Solid modeling
Three-dimensional displays
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Summary: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.
ISSN:2575-7075
DOI:10.1109/CVPR42600.2020.00273