Computed tomography (CT) is a widely-used diagnostic image modality routinely used for assessing anatomical tissue characteristics. However, non-standardized imaging protocols are commonplace, which poses a fundamental challenge in large-scale cross-center CT image analysis. This project propsed a GAN based model to standardized the CT images in terms of texture features.
https://arxiv.org/abs/2004.01307
In generator part, a U-net is used and the traditional CNN is used in discriminator. But insted of having single generator and discriminator, the GANai has multiple G and D. At the end of the training best G and D is finalized.
2.448 chest CT image slices of lung cancer patients were used with different slice thickness and reconstruction kernels. Finally 14,958 image path pairs are generated using data augmentation technique.
"ganai_delivery.py" contains the implemntation of the paper and "test_train_script.sh" contains the arguments to run the model.