MedGAN: Medical Image Translation using GANs
This addresses the problem of specialized or non-end-to-end methods in medical image analysis, offering a generalizable solution for tasks such as image translation and denoising, though it appears incremental by building on existing GAN advances.
The paper tackles medical image-to-image translation by proposing MedGAN, an end-to-end framework that merges adversarial training with non-adversarial losses and a new generator architecture, achieving superior performance in tasks like PET-CT translation and MR motion artifact correction as validated by radiologists and quantitative evaluations.
Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches.