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+# 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.