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

翻译：Pix2Pix 监督 Pix2Pix 和不受监督的周期一致性是主导医学图像到图像翻译领域的两种模式。 但是,两种模式都不是理想的。 Pix2Pix 模式的性能极佳。 但是,它需要配对的像素匹配图像, 由于呼吸运动或配对图像获得的时间之间的解剖变化, 这些图像并不总是可以实现。 循环一致性模式与培训数据不那么严格, 并且对未调适或错配错的图像运作良好。 但是, 它的性能可能不是最理想的。 为了打破现有模式的两难困境, 我们提议了一个名为 RegGAN 的未经监督的新模式。 它基于“ 损失校正校正” 理论。 在 RegGAN 中, 误差的目标图像被视为噪音标签, 并且用额外的登记网络来适应不调和的噪音分布。 目标是在图像到映射的图像网络中,我们将RegGAN 的更精确的代校正(RegG-AN) 的代号都比更不易地用新的数据方法。