Multi-Scale Texture Loss for CT denoising with GANs
Francesco Di Feola Lorenzo Tronchin Valerio Guarrasi Paolo Soda
Generative Adversarial Networks (GANs) have proved as a powerful framework
for denoising applications in medical imaging. However, GAN-based denoising
algorithms still suffer from limitations in capturing complex relationships
within the images. In this regard, the loss function plays a crucial role in
guiding the image generation process, encompassing how much a synthetic image
differs from a real image. To grasp highly complex and non-linear textural
relationships in the training process, this work presents a loss function that
leverages the intrinsic multi-scale nature of the Gray-Level-Co-occurrence
Matrix (GLCM). Although the recent advances in deep learning have demonstrated
superior performance in classification and detection tasks, we hypothesize that
its information content can be valuable when integrated into GANs' training. To
this end, we propose a differentiable implementation of the GLCM suited for
gradient-based optimization. Our approach also introduces a self-attention
layer that dynamically aggregates the multi-scale texture information extracted
from the images. We validate our approach by carrying out extensive experiments
in the context of low-dose CT denoising, a challenging application that aims to
enhance the quality of noisy CT scans. We utilize three publicly available
datasets, including one simulated and two real datasets. The results are
promising as compared to other well-established loss functions, being also
consistent across three different GAN architectures. The code is available at:
https://github.com/FrancescoDiFeola/DenoTextureLoss