Datasets:
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license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- medical
- polyp-segmentation
- colonoscopy
- gastrointestinal
size_categories:
- n<1K
pretty_name: CVC-ClinicDB
---
# CVC-ClinicDB: Colonoscopy Polyp Segmentation Dataset
## Dataset Description
CVC-ClinicDB is a colonoscopy polyp segmentation dataset containing 612 frames extracted from 29 colonoscopy sequences with corresponding ground truth segmentation masks.
- **Task**: Binary segmentation (polyp vs. background)
- **Modality**: Colonoscopy
- **Format**: PNG images (384x288 pixels) with binary masks
- **Splits**: Training, Validation, and Test sets
## Dataset Structure
```
CVC-ClinicDB/
├── train/
│ ├── images/ # Training images
│ └── masks/ # Training masks
├── validation/
│ ├── images/ # Validation images
│ └── masks/ # Validation masks
└── test/
├── images/ # Test images
└── masks/ # Test masks
```
## Citation
If you use this dataset, please cite:
```bibtex
@article{bernal2015wm,
title={WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians},
author={Bernal, Jorge and S{'a}nchez, F Javier and Fern{'a}ndez-Esparrach, Gloria and Gil, Debora and Rodr{\'\i}guez, Cristina and Vilari{\~n}o, Fernando},
journal={Computerized Medical Imaging and Graphics},
volume={43},
pages={99--111},
year={2015},
publisher={Elsevier}
}
```
**Dataset Split**: This train/validation/test split is created by the following study. Please find more details in:
```bibtex
@article{chang2024esfpnet,
title={ESFPNet: Efficient Stage-Wise Feature Pyramid on Mix Transformer for Deep Learning-Based Cancer Analysis in Endoscopic Video},
author={Chang, Qi and Ahmad, Danish and Toth, Jennifer and Bascom, Rebecca and Higgins, William E},
journal={Journal of Imaging},
volume={10},
number={8},
pages={191},
year={2024},
publisher={MDPI}
}
```
## Usage
```python
from datasets import load_dataset
# Load dataset
dataset = load_dataset("Angelou0516/CVC-ClinicDB")
# Access splits
train_data = dataset['train']
val_data = dataset['validation']
test_data = dataset['test']
# Access a sample
sample = train_data[0]
image = sample['file_name'] # Image
mask = sample['mask_file_name'] # Segmentation mask
```
## License
Please refer to the original CVC-ClinicDB dataset license and citation requirements.
## Links
- Original Dataset: https://polyp.grand-challenge.org/CVCClinicDB/
- Paper: https://www.sciencedirect.com/science/article/pii/S0895611115000567
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