An OpenMind for 3D medical vision self-supervised learning
Paper
• 2412.17041 • Published
Error code: ClientConnectionError
OpenMind2D is a 2D medical imaging dataset derived from the OpenMind dataset. It contains 335,754 2D slices extracted from 3D brain MRI volumes in three anatomical orientations (axial, sagittal, coronal).
This dataset is derived from the OpenMind dataset (Dufumier et al., 2024), which contains 114,000 3D brain MRI volumes from 800 OpenNeuro datasets.
OpenMind2D/
├── metadata.parquet # Primary metadata
├── train/ # All images
│ ├── 00000001_000.jpg
│ └── ...
└── README.md
image: 256×256 JPEG brain MRI sliceorientation: axial, sagittal, or coronalvolume_id: Volume identifierunique_id: Original OpenMind volume IDmodality: MRI sequence typesplit: train/validation/testage: Subject agesex: Subject sexmanufacturer: Scanner manufacturerfrom datasets import load_dataset
# Load dataset
dataset = load_dataset("liamchalcroft/OpenMind2D")
train_data = dataset['train']
# Get sample
sample = train_data[0]
image = sample['image']
orientation = sample['orientation']
modality = sample['modality']
# Filter by modality or orientation
t1_data = dataset.filter(lambda x: x['modality'] == 'T1w')
axial_data = dataset.filter(lambda x: x['orientation'] == 'axial')
If you use this dataset, please cite the original OpenMind work:
@article{dufumier2024openmind,
title = {OpenMind: A Large-Scale Dataset for Self-Supervised Learning in Medical Imaging},
author = {Dufumier, Basile and others},
journal = {arXiv preprint arXiv:2412.17041},
year = {2024},
url = {https://arxiv.org/abs/2412.17041}
}
This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0), consistent with the original OpenMind dataset.