File size: 3,978 Bytes
86d9d66 fdadc96 71e2849 fdadc96 86d9d66 fdadc96 86d9d66 fdadc96 86d9d66 fdadc96 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
---
annotations_creators:
- machine-generated
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
pretty_name: OpenMind2D
size_categories:
- 100K<n<1M
source_datasets:
- AnonRes/OpenMind
task_categories:
- image-classification
- image-to-text
- zero-shot-image-classification
task_ids:
- multi-class-image-classification
- image-captioning
- visual-question-answering
tags:
- medical
- neuroimaging
- brain
- mri
- 3d-to-2d
- computer-vision
- healthcare
paperswithcode_id: openmind
dataset_info:
features:
- name: image
dtype: image
- name: orientation
dtype: string
- name: volume_id
dtype: int32
- name: slice_id
dtype: int32
- name: slice_coord
dtype: int32
- name: split
dtype: string
- name: unique_id
dtype: string
- name: modality
dtype: string
- name: image_quality_score
dtype: float32
- name: age
dtype: float32
- name: sex
dtype: string
- name: health_status
dtype: string
- name: manufacturer
dtype: string
- name: magnetic_field_strength
dtype: float32
- name: repetition_time
dtype: float32
- name: echo_time
dtype: float32
- name: width
dtype: int32
- name: height
dtype: int32
- name: format
dtype: string
- name: file_size
dtype: int32
splits:
- name: train
num_bytes: 16787700000
num_examples: 335754
download_size: 11751390000
dataset_size: 16787700000
---
# OpenMind2D: 2D Brain MRI Slices
OpenMind2D is a 2D medical imaging dataset derived from the [OpenMind dataset](https://huggingface.co/datasets/AnonRes/OpenMind). It contains 335,754 2D slices extracted from 3D brain MRI volumes in three anatomical orientations (axial, sagittal, coronal).
## Dataset Statistics
- **Total Images**: 335,754
- **Resolution**: 256×256 pixels
- **Format**: JPEG
- **Size**: ~11.7 GB
- **Splits**: Train (70%), Validation (20%), Test (10%)
- **Orientations**: Axial, sagittal, coronal
- **Modalities**: T1w, T2w, FLAIR, DWI, and 19+ additional MRI types
## Source
This dataset is derived from the OpenMind dataset ([Dufumier et al., 2024](https://arxiv.org/abs/2412.17041)), which contains 114,000 3D brain MRI volumes from 800 OpenNeuro datasets.
### Processing
1. Slice extraction from three anatomical orientations
2. Isotropic resampling to 1mm³ spacing
3. Intensity normalization (1st-99th percentile clipping)
4. Resize to 256×256 pixels
5. JPEG compression with metadata preservation
## Dataset Structure
```
OpenMind2D/
├── metadata.parquet # Primary metadata
├── train/ # All images
│ ├── 00000001_000.jpg
│ └── ...
└── README.md
```
### Key Metadata Fields
- `image`: 256×256 JPEG brain MRI slice
- `orientation`: axial, sagittal, or coronal
- `volume_id`: Volume identifier
- `unique_id`: Original OpenMind volume ID
- `modality`: MRI sequence type
- `split`: train/validation/test
- `age`: Subject age
- `sex`: Subject sex
- `manufacturer`: Scanner manufacturer
## Usage
```python
from 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')
```
## Citation
If you use this dataset, please cite the original OpenMind work:
```bibtex
@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}
}
```
## License
This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0), consistent with the original OpenMind dataset. |