# Cardiac Segmentation ACDC

Deep learning-based cardiac segmentation using PyTorch, MONAI, and U-Net models.

## About

This project leverages deep learning models for cardiac segmentation, particularly for the ACDC dataset. The goal is to segment the heart's anatomical structures to aid in medical analysis.

### Features:

- **2D and 3D U-Net models** for segmentation.

- **Attention mechanisms** for improved segmentation accuracy.

- **Support for both 2D and 3D datasets** (using MONAI and PyTorch).

## Installation

### Prerequisites:

- Python 3.x

- PyTorch

- MONAI

- other Python dependencies from `requirements.txt`

### Setup:

1. Clone the repository:

    ```bash

    git clone https://github.com/arkanandi/Cardiac_Segmentation_ACDC.git

    cd Cardiac_Segmentation_ACDC

    ```

2. Install the required Python libraries:

    ```bash

    pip install -r requirements.txt

    ```

3. Download the ACDC dataset and place it in the appropriate folder (see the dataset instructions in the repository for details).

4. To train the model, run:

    ```bash

    python train_2d.py

    # or

    python train_3d.py

    ```

5. To make predictions:

    ```bash

    python predict_2d.py

    # or

    python predict_3d.py

    ```

## Usage

Once you have trained the model, you can use it to predict cardiac structures on new datasets.

## Contributing

Feel free to fork the repository and submit pull requests. Issues and suggestions are always welcome.

1. Fork this repository.

2. Clone your fork:

    ```bash

    git clone https://github.com/your-username/Cardiac_Segmentation_ACDC.git

    ```

3. Create a new branch:

    ```bash

    git checkout -b feature-name

    ```

4. Make changes and commit:

    ```bash

    git commit -am 'Add new feature'

    ```

5. Push to your fork:

    ```bash

    git push origin feature-name

    ```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.