Breaking the Dilemma of Medical Image-to-image Translation

TL;DR

RegGAN introduces a novel unsupervised mode for medical image-to-image translation that effectively handles misaligned and unpaired data by integrating a registration network, outperforming existing methods like Pix2Pix and CycleGAN.

Contribution

The paper proposes RegGAN, a new unsupervised approach combining image translation and registration, improving performance on misaligned medical images without requiring paired data.

Findings

RegGAN surpasses Pix2Pix on aligned data.

RegGAN outperforms CycleGAN on misaligned or unpaired data.

RegGAN is robust to noise and versatile across scenarios.

Abstract

Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that dominate the field of medical image-to-image translation. However, neither modes are ideal. The Pix2Pix mode has excellent performance. But it requires paired and well pixel-wise aligned images, which may not always be achievable due to respiratory motion or anatomy change between times that paired images are acquired. The Cycle-consistency mode is less stringent with training data and works well on unpaired or misaligned images. But its performance may not be optimal. In order to break the dilemma of the existing modes, we propose a new unsupervised mode called RegGAN for medical image-to-image translation. It is based on the theory of "loss-correction". In RegGAN, the misaligned target images are considered as noisy labels and the generator is trained with an additional registration network to fit the misaligned noise distribution adaptively. The goal is to search for the common optimal solution to both image-to-image translation and registration tasks. We incorporated RegGAN into a few state-of-the-art image-to-image translation methods and demonstrated that RegGAN could be easily combined with these methods to improve their performances. Such as a simple CycleGAN in our mode surpasses latest NICEGAN even though using less network parameters. Based on our results, RegGAN outperformed both Pix2Pix on aligned data and Cycle-consistency on misaligned or unpaired data. RegGAN is insensitive to noises which makes it a better choice for a wide range of scenarios, especially for medical image-to-image translation tasks in which well pixel-wise aligned data are not available