---
language:
- en
tags:
- medical
- brain
- mri
- neuroimaging
- pathology
- object-detection
- bounding-boxes
- vision-language
- multimodal
- fine-tuning
task_categories:
- object-detection
- image-to-text
- visual-question-answering
modality:
- image
- text
size_categories:
- 100K<n<1M
pretty_name: BrainBox
---

# BrainBox

A unified medical imaging dataset containing 134,201 brain MRI slices with bounding box annotations, designed for fine-tuning vision-language models (VLMs) on medical image localization tasks.

## Dataset Summary

BrainBox consolidates multiple 2D and 3D medical imaging datasets into a standardized 2D format optimized for VLM training. Each image is paired with structured metadata and precise bounding box annotations for pathological findings.

- **Total Images**: 134,201 2D MRI slices
- **Source Datasets**: 8 publicly available medical imaging datasets
- **Modalities**: T1w, T1c, T2w, FLAIR, DWI, ADC, TRACE
- **Orientations**: Axial, Coronal, Sagittal
- **Pathologies**: Stroke (68,136), Glioma (57,964), Meningioma (4,168), Pituitary Adenoma (3,933)

## Dataset Purpose

This dataset serves as a fine-tuning corpus for vision-language models to perform:
- Medical image interpretation and localization
- Pathology detection with spatial grounding
- Multimodal medical question answering
- Cross-modal medical image understanding

## Data Processing

- **3D to 2D Conversion**: 3D volumes converted to optimal 2D slices using intelligent slice selection
- **Standardization**: Unified schema across heterogeneous source datasets
- **Quality Control**: Verified image paths and validated bounding box annotations
- **Format**: JPG images (256×256) with JSON bounding box metadata

## Usage

```python
from datasets import load_dataset
import json

dataset = load_dataset("liamchalcroft/brainbox")

# Example: Load an image with its annotations
sample = dataset["train"][0]
image = sample["image"]
diagnosis = sample["diagnosis"] 
bboxes = json.loads(sample["bounding_boxes"])  # Parse JSON string
```

## Schema

| Field | Type | Description |
|-------|------|-------------|
| `image` | Image | 2D MRI slice (JPG format) |
| `diagnosis` | string | Primary medical diagnosis |
| `pathology_subtype` | string | Specific pathology classification |
| `lesion_type` | string | General lesion category (neoplasm/vascular_lesion) |
| `bounding_boxes` | string | JSON array of lesion bounding boxes |
| `modality` | string | MRI sequence type |
| `orientation` | string | Slice orientation |
| `has_pathology` | bool | Presence of pathological findings |
| `subject_id` | string | Anonymized subject identifier |
| Additional fields | - | Demographics, imaging parameters, anatomical locations |

## Bounding Box Format

```json
[
  {
    "x_min": 128, "y_min": 62,
    "x_max": 139, "y_max": 66,
    "width": 11, "height": 4, "area": 44
  }
]
```

## Applications

- **VLM Fine-tuning**: Train vision-language models for medical image understanding
- **Object Detection**: Develop pathology localization models
- **Medical AI**: Build diagnostic assistance systems
- **Research**: Cross-modal medical image analysis studies

## Data Distribution

- **Vascular Lesions**: 68,136 images (acute, chronic, subacute stroke)  
- **Neoplasms**: 66,065 images (glioma, meningioma, pituitary adenoma)
- **Balanced Orientations**: ~33% each (axial, coronal, sagittal)
- **Multi-modal**: 7 MRI sequence types

## Limitations

- Converted to 2D format (original 3D context reduced)
- Heterogeneous source data quality
- Limited demographic metadata coverage
- Bounding boxes simplified from original segmentation masks

## Citation

If you use this dataset, please cite the original source datasets. This is a processed compilation for VLM research purposes.

## License

Individual source datasets retain their original licenses. Verify licensing terms for your specific use case.