MedGAN: Medical Image Translation using GANs

TL;DR

MedGAN is an end-to-end framework using GANs with novel loss functions and a new generator architecture, CasNet, to improve medical image translation tasks like PET-CT translation, MR correction, and PET denoising.

Contribution

Introduces MedGAN, a unified end-to-end GAN-based framework with a new generator and combined loss functions for medical image translation.

Findings

Outperforms existing methods in various medical image translation tasks.

Produces sharper and more accurate translated images.

Validated by radiologist perceptual analysis and quantitative metrics.

Abstract

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.