---
license: cc-by-4.0
task_categories:
  - image-segmentation
  - object-detection
tags:
  - biology
  - medical-imaging
  - microscopy
  - cell-segmentation
  - CAR-T
  - immunological-synapse
size_categories:
  - n<1K
---

# CAR-T/NK Immunological Synapse Dataset

## Dataset Description

This dataset contains fluorescence microscopy images of Chimeric Antigen Receptor (CAR)-T and NK cell immunological synapses (IS) with corresponding instance segmentation masks. The images were acquired from patient-derived CAR-T/NK cell samples stained with multiple fluorescent markers including Perforin, P-Zeta, F-actin, and IgG.

The dataset is designed for training and evaluating object detection and instance segmentation models on biomedical cell microscopy images.

## Dataset Structure

```
data_hf/
├── images/              # 156 fluorescence microscopy images (full dataset)
├── masks/               # 156 instance segmentation masks (full dataset)
├── train/               # 93 training images
├── train_annotation/    # 93 training masks
├── test/                # 31 test images
├── test_annotation/     # 31 test masks
├── val/                 # 32 validation images
└── val_annotation/      # 32 validation masks
```

## Dataset Statistics

| Split      | Images | Masks |
|------------|--------|-------|
| Train      | 93     | 93    |
| Test       | 31     | 31    |
| Validation | 32     | 32    |
| **Total**  | **156**| **156**|

## Image Properties

- **Resolution**: 1024 x 1024 pixels
- **Format**: PNG
- **Modality**: Multi-channel fluorescence microscopy
- **Fluorescent markers**: AF647, AF488 (Perforin), AF568 (P-Zeta), AF405 (F-actin), and others depending on CAR construct

## Annotation Details

Each mask is an instance segmentation annotation where individual cells are labeled with unique pixel values. The masks enable both bounding box detection and pixel-level instance segmentation tasks.

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/cart-nk-is-dataset")
```

Or directly load images:

```python
from PIL import Image
import os

image = Image.open("train/example.png")
mask = Image.open("train_annotation/example.png")
```

## Citation

If you use this dataset, please cite:

```bibtex
@article{zhang2025data,
  title={Data Augmentation for High-Fidelity Generation of CAR-T/NK Immunological Synapse Images},
  author={Zhang, Xiang and Zhang, Boxuan and Naghizadeh, Alireza and Mohamed, Mohab and Liu, Dongfang and Tang, Ruixiang and Metaxas, Dimitris and Liu, Dongfang},
  journal={Frontiers},
  year={2025}
}
```

## License

This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.