FusGAN: GAN-Based Ultrasound Simulation from CT Slices

Overview

Features

• Ultrasound Simulation Generation: Convert CT slices into simulated ultrasound images.
• Mask Input: Utilize masks to define the transducer placement and orientation guide the simulation process and focus on specific regions.
• Customizable Settings: Adjust parameters to fit different use cases and requirements.

Installation

1. Clone the Repository:
   bash

   1git clone https://github.com/aconesac/fusGAN.git 2cd fusGAN
2. Install Dependencies:
   It is recommended to use a virtual environment. Install the required Python packages with:
   pip install -r requirements.txt
   Make sure you have the necessary libraries for GANs and image processing, such as TensorFlow, NumPy, scikit-learn.

Usage

1. Prepare Your Data:
   • Place your CT slices and corresponding mask images in the data/ct_slices and data/tr_masks directories, respectively. Place the output simulations for training in data/pi_maps_.
2. Train the GAN:
   python train_gan.py --ct_data_path=data/ct_slices --mask_data_path=data/masks --sim_path=data/pii
   This command trains the GAN model using your CT and mask data and saves the trained model in the models/ directory.
3. Generate Ultrasound Simulations:
   python generateSimulation.py --ct_image_path=data/ct_slices/example_ct_slice.png --mask_path=data/masks/example_mask.png --model_path=models/trained_gan_model.h5 --output_path=results/
   This command generates an ultrasound simulation for a given CT slice and mask, saving the result in the results/ directory.

Examples

• Example Input: data/ct_slices/example_ct_slice.png, data/masks/example_mask.png, data/pii/sim_out.png
• Example Output: results/simulated_ultrasound.png

Notes

• Ensure your input CT slices and masks are properly aligned and preprocessed for optimal results.
• The performance and quality of the generated ultrasound images depend on the quality and quantity of the training data.

Contributing

License

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