# Gastric Cancer Tissue Segmentation Dataset  

**License:** [Apache-2.0](https://opensource.org/licenses/Apache-2.0)  

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## Overview  

This dataset is designed for **tissue segmentation** in gastric cancer cases. It consists of **100 Regions of Interest (ROIs)** extracted from Whole Slide Images (WSIs) of 100 gastric cancer cases.  

![Gastric Cancer Segmentation](https://cdn-uploads.huggingface.co/production/uploads/65f978b2119e70f4dbb7c6b1/zDW5HvtTGYS2xSUGQ5kAJ.png)

### Tissue Types  
Six tissue types are annotated:  
1. **Tumor**  
2. **Lymphoid stroma**  
3. **Desmoplastic stroma**  
4. **Smooth muscle**  
5. **Necrosis**  
6. **Others**  

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## Data Source  

The original WSIs are sourced from the **TCGA (The Cancer Genome Atlas)** database.  

- **Mean size of ROIs**: 4655 × 5276 pixels  

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## Annotation Process  

- The annotated ROIs achieved a **78% one-time acceptance rate**.  
- The remaining annotations were **accepted after one revision**.  
- Pathologists performed minor corrections on **8.4% of all pixels** in total.  

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## Data Organization  

The dataset includes:  
1. **ROIs (image patches)**:  
   - Saved as `.png` files under the corresponding folders.  
2. **Annotations**:  
   - Each ROI's annotation is saved as a `.txt` file under the corresponding folders.  
   - The annotation is a pixel-wise matrix with the following values:  
     - **1**: Tumor  
     - **2**: Lymphoid stroma  
     - **3**: Desmoplastic stroma  
     - **4**: Smooth muscle  
     - **5**: Necrosis  
     - **6**: Others  
     - **-1**: Equal to 6 (others)  

---

## Usage and Restrictions  

- This dataset is **for research purposes only**.  
- **Commercial use is strictly prohibited**.  

If you use this dataset in your research, you must cite the following publication:  

```bibtex
@article{gao2022unsupervised,
  title={Unsupervised representation learning for tissue segmentation in histopathological images: From global to local contrast},
  author={Gao, Zeyu and Jia, Chang and Li, Yang and Zhang, Xianli and Hong, Bangyang and Wu, Jialun and Gong, Tieliang and Wang, Chunbao and Meng, Deyu and Zheng, Yefeng and others},
  journal={IEEE Transactions on Medical Imaging},
  volume={41},
  number={12},
  pages={3611--3623},
  year={2022},
  publisher={IEEE}
}
```