README.md

---
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.