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Cited by 99Intracranial Aneurysms: Treatment and Complications3D Shape Modeling and AnalysisMedical Image Segmentation Techniques

IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning

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Yang, Xi, Xia, Ding, Kin, Taichi, Igarashi, Takeo·2020·2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr42600.2020.00273
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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.

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  • VesselMNIST3D is a 3D dataset based on the IntrA dataset, containing 103 3D models of entire brain vessels, with 1,694 healthy vessel segments and 215 aneurysm segments.

    “The VesselMNIST3D is based on an open-access 3D intracranial aneurysm dataset, IntrA34, containing 103 3D models (meshes) of entire brain vessels collected by reconstructing MRA images. 1,694 healthy vessel segments and 215 aneurysm segments are generated automatically from the complete models.”

    From MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification (2023) · cites this paper as [34]

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Cited by

Sorted by citations

Authors
Yang, X., Xia, D., Kin, T., & Igarashi, T.
Affiliations
The University of Tokyo (JP)
ORCID
Venue
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2020-06
Pages
2653-2663
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr42600.2020.00273
OpenAlex
W3035168834
Cited by
99
References
2
Citations by year
2026: 15 · 2025: 23 · 2024: 25 · 2023: 17 · 2022: 11 · 2021: 6 · 2020: 2
FWCI
36.94
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
Japan Agency for Medical Research and Development (JP18he1602001)
Topics
  • Health Sciences › Medicine › Neurology › Intracranial Aneurysms: Treatment and Complications
  • Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
intracranial aneurysm, 3D medical imaging, aneurysm classification, benchmark, surface normal estimation, surface reconstruction

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