On the Use of Perceptual Loss for Fine Structure Generation : Illustration on Lung MR to CT Synthesis

The use of perceptual loss has emerged as a prominent technique for enhancing image-to-image translation tasks by capturing high-level visual similarities between the ground truth and predicted images. In this study, we investigate fine structure generation in MR to CT synthesis and focus on evaluating the effectiveness of perceptual loss under different parameters. Specifically, we assess the impacts of architecture and training data of the pre-trained network, as well as the effects of data normalization and feature selection. To validate our findings, we generate synthetic lung CT images using GANs from an MR dataset of 120 patients, and evaluate them against the corresponding registered CT scans. Our evaluation metrics include measures of structural and visual image quality as well as a taskspecific metric using the segmentation of airway bronchi in the synthesized CT images. Our work highlights the importance of maintaining a balanced integration of low-level and high-level features to achieve high-quality fine structure generation. Additionally, we compare perceptual loss pre-trained on either natural images or medical images, and our results suggest that the dataset used to pre-train the model does not necessarily need to rely on generic medical data, as it is only used as a fixed feature extractor in the context of perceptual loss. By illuminating the impact of latent space computation and feature selection, our study offers valuable insights into improving fine structure generation in MR to CT synthesis and image-to-image translation in general.