SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis
This work addresses the need for high-quality synthetic medical images that retain diagnostic details, potentially improving data augmentation for segmentation tasks, though it appears incremental by building on existing GAN frameworks.
The paper tackled the problem of medical image synthesis by proposing SkrGAN, which incorporates a sketch prior constraint to preserve fine foreground structures like vessels and skeletons, achieving state-of-the-art results across multiple modalities such as retinal fundus, X-Ray, CT, and MRI.
Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks. However, most of existing methods merely consider the global contextual information and ignore the fine foreground structures, e.g., vessel, skeleton, which may contain diagnostic indicators for medical image analysis. Inspired by human painting procedure, which is composed of stroking and color rendering steps, we propose a Sketching-rendering Unconditional Generative Adversarial Network (SkrGAN) to introduce a sketch prior constraint to guide the medical image generation. In our SkrGAN, a sketch guidance module is utilized to generate a high quality structural sketch from random noise, then a color render mapping is used to embed the sketch-based representations and resemble the background appearances. Experimental results show that the proposed SkrGAN achieves the state-of-the-art results in synthesizing images for various image modalities, including retinal color fundus, X-Ray, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). In addition, we also show that the performances of medical image segmentation method have been improved by using our synthesized images as data augmentation.