We consider medical image transformation problems where a grayscale image is transformed into a color image. The colorized medical image should have the same features as the input image because extra synthesized features can increase the possibility of diagnostic errors. In this paper, to secure colorized medical images and improve the quality of synthesized images, as well as to leverage unpaired training image data, a colorization network is proposed based on the cycle generative adversarial network (CycleGAN) model, combining a perceptual loss function and a total variation (TV) loss function. Visual comparisons and experimental indicators from the NRMSE, PSNR, and SSIM metrics are used to evaluate the performance of the proposed method. The experimental results show that GAN-based style conversion can be applied to colorization of medical images. As well, the introduction of perceptual loss and TV loss can improve the quality of images produced as a result of colorization better than the result generated by only using the CycleGAN model.

我们考虑将灰度图像转换为彩色图像的医学图像转换问题。彩色医学图像应具有与输入图像相同的特征，因为额外的合成特征会增加诊断错误的可能性。为了保护彩色医学图像并提高合成图像的质量，并利用不成对的训练图像数据，基于循环生成对抗网络（CycleGAN）模型，结合感知损失函数和总变化（TV）损失函数。NRMSE，PSNR和SSIM指标的视觉比较和实验指标用于评估该方法的性能。实验结果表明，基于GAN的样式转换可以应用于医学图像的着色。同样，与仅使用CycleGAN模型产生的结果相比，引入感知损失和TV损失可以更好地改善由于着色而产生的图像质量。