---
license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- medical
- CT
- segmentation
- Couinaud_Liver
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: train.jsonl
---

# Couinaud Liver Segmentation Dataset

## Dataset Description

The Couinaud Liver Segmentation dataset for liver segmentation based on Couinaud classification. This dataset contains CT scans with dense segmentation annotations.

### Dataset Details

- **Modality**: CT
- **Target**: 8 liver segments (Couinaud classification)
- **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": "CT",
  "dataset": "Couinaud_Liver",
  "official_split": "train",
  "patient_id": "patient_id"
}
```

## Usage

### Load Metadata

```python
from datasets import load_dataset

# Load the dataset
ds = load_dataset("Angelou0516/couinaud-liver")

# 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/couinaud-liver",
    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{couinaud_liver,
  title={Couinaud Liver Segmentation Dataset},
  year={2023}
}
```

## License

CC-BY-4.0

## Dataset Homepage

https://www.ircad.fr/