IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning

2020/03/02 by Xi Yang, Ding Xia, Yang, Xi +5 · 11 citations
Computer Science · Engineering · Mathematics · #3D Shape Modeling and Analysis #Anatomy and Medical Technology #Medical Image Segmentation Techniques #cs.CV #cs.LG #eess.IV #stat.ML

paper · pdf · doi:10.48550/arxiv.2003.02920

Accepted by cvpr2020, camera-ready version will be uploaded later

arxiv created 2020/04/06 · arxiv updated 2020/04/07

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/intra3d2019/IntrA.

Citations

• Thingi10K: A Dataset of 10,000 3D-Printing Models
• A large annotated medical image dataset for the development and evaluation of segmentation algorithms
• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Cited by

• Graph Deep Learning for Intracranial Aneurysm Blood Flow Simulation and Risk Assessment
• TACO-Net: Topological Signatures Triumph in 3D Object Classification
• TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis
• Multi-view Normal and Distance Guidance Gaussian Splatting for Surface Reconstruction
• A Survey of Medical Point Cloud Shape Learning: Registration, Reconstruction and Variation
• Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models
• Recursive Variational Autoencoders for 3D Blood Vessel Generative Modeling
• Vector Representations of Vessel Trees
• Generative Data Augmentation for Object Point Cloud Segmentation
• SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation
• Aneumo: A Large-Scale Multimodal Aneurysm Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks

Related

• 3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-branch Learning
• A Two-step Surface-based 3D Deep Learning Pipeline for Segmentation of Intracranial Aneurysms
• Integrated Deep Learning Model for the Detection, Segmentation, and Morphologic Analysis of Intracranial Aneurysms Using CT Angiography
• Deep Learning-based Segmentation of Cerebral Aneurysms in 3D TOF-MRA using Coarse-to-Fine Framework
• A vascular synthetic model for improved aneurysm segmentation and detection via Deep Neural Networks