# ColorMedGAN: A Semantic Colorization Framework for Medical Images
## 1 Introduction
Therefore, the use of extracted features to calculate non-local similarity has become a popular method in recent years [21; 22]. [PERSON] et al. [23] have also incorporated local detail perception with semantic segmentation to improve the quality of colorization. One of the few studies on colorizing medical images involved the use of transfer functions to map intensity values to colors, as explored by [PERSON] et al. [24]. However, these methods can be cumbersome and time-consuming, and often require manual intervention. [PERSON] et al. [5] addressed this issue by embedding color information in each pixel of the subject image, resulting in the potential for better depiction of anatomical information compared to traditional monochrome images. Another approach, used by [PERSON] et al. [25], involved the use of VGG19 (Very Deep Convolutional NetWorks for Large-Scale Image Recognition) [26] and adaptive target image retrieval to generate high-quality color medical images. This method incorporated Y-loss and content-loss to maintain texture structure and image color loss to maintain color invariance between the generated image and the reference image. [PERSON] et al. [27] used an image colorization method based on Gabor filtering and Welsh colorization to extract local spatial and frequency domain information from grayscale images and render images with distinct texture features in the filtered images. However, these methods are often dependent on the performance of retrieval algorithms or the quality of reference images, and the resulting images can look more like pseudo-colored images than the actual situation. [PERSON] et al. [28] used 2D style migration for colorizing 3D medical images, demonstrating multi-modal body colorization using only one 2D style image. This approach can be used to create better 3D visualizations without the need for time-consuming transfer functions. [PERSON] et al. [2] proposed a fully automatic spatial mask-guided colorization with a generative adversarial network framework for medical image colorization. This method generates colorized images with fewer artifacts by introducing spatial masks, which encourage the network to focus on the colorization of foreground regions rather than the entire image. Although the above methods generate anatomical-style images from MRI or grayscale images, they may lose a significant amount of information inherent in medical imaging. Additionally, some of these methods rely on one-to-one paired datasets, such as MRI and anatomical colored images in [28], which requires considerable manual screening work and carries the risk of misalignment. Medical images are known for their low variation in tissue and high complexity in texture, making them prone to color bleeding and loss of texture details during colorization. Existing colorization methods for medical images often require paired data or manual alignment between different modalities, which can result in unclear boundaries and a single colorization effect. Our experiments demonstrate that current models cannot generate high-quality colorized medical images. To address these challenges, we propose a novel approach that treats medical image colorization as a cross-modal transfer problem in color space. Our method constrains color generation with rich semantic and edge information to eliminate the need for paired data and produce high-quality images. CycleGAN (The Cycle Generative Adversarial Network) has emerged as a powerful tool for medical image translation, and recent research has explored its potential for medical image augmentation and unsupervised translation. Recently, some studies [29; 30] have combined CycleGAN's advantages with the constraints of local structure and feature details to improve medical image augmentation. Cycle GAN-based models are also widely applied to medical image translation, with [PERSON] et al. [31] proposing a novel unsupervised translation framework called Cycle-MedGAN.