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---
license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- medical
- MRI
- segmentation
- CrossMoDA2021
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: train.jsonl
---
# CrossMoDA 2021 Dataset
## Dataset Description
The CrossMoDA 2021 dataset for vestibular schwannoma and cochlea segmentation. This dataset contains MRI (ceT1) scans with dense segmentation annotations.
### Dataset Details
- **Modality**: MRI (ceT1)
- **Target**: vestibular schwannoma, cochlea
- **Format**: NIfTI (.nii.gz)
### Dataset Structure
Each sample in the JSONL file contains:
```json
{
"image": "path/to/image.nii.gz",
"mask": "path/to/mask.nii.gz",
"label": ["organ1", "organ2", ...],
"modality": "MRI",
"dataset": "CrossMoDA2021",
"official_split": "train",
"patient_id": "patient_id"
}
```
## Usage
### Load Metadata
```python
from datasets import load_dataset
# Load the dataset
ds = load_dataset("Angelou0516/crossmoda2021")
# Access a sample
sample = ds['train'][0]
print(f"Patient ID: {sample['patient_id']}")
print(f"Image: {sample['image']}")
print(f"Mask: {sample['mask']}")
print(f"Labels: {sample['label']}")
```
### Load Images
```python
from huggingface_hub import snapshot_download
import nibabel as nib
import os
# Download the full dataset
local_path = snapshot_download(
repo_id="Angelou0516/crossmoda2021",
repo_type="dataset"
)
# Load a sample
sample = ds['train'][0]
image = nib.load(os.path.join(local_path, sample['image']))
mask = nib.load(os.path.join(local_path, sample['mask']))
# Get numpy arrays
image_data = image.get_fdata()
mask_data = mask.get_fdata()
print(f"Image shape: {image_data.shape}")
print(f"Mask shape: {mask_data.shape}")
```
## Citation
```bibtex
@article{crossmoda2021,
title={Cross-Modality Domain Adaptation for Medical Image Segmentation},
year={2023}
}
```
## License
CC-BY-4.0
## Dataset Homepage
https://crossmoda.grand-challenge.org/
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