# SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis

arXiv: https://arxiv.org/abs/1908.04346 · HTML: https://mlsanity.com/paper/1908.04346/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 14 of 14)

- [Depth-Aware Generative Adversarial Network for Talking Head Video Generation](https://mlsanity.com/paper/2203.06605/) (2022)
- [Return of Unconditional Generation: A Self-supervised Representation Generation Method](https://mlsanity.com/paper/2312.03701/) (2023)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis](https://mlsanity.com/paper/2204.06929/) (2022)
- [BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys](https://mlsanity.com/paper/2310.10765/) (2023)
- [Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation](https://mlsanity.com/paper/2103.03781/) (2021)
- [Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations](https://mlsanity.com/paper/2306.10941/) (2023)
- [Medical Image Synthesis via Fine-Grained Image-Text Alignment and Anatomy-Pathology Prompting](https://mlsanity.com/paper/2403.06835/) (2024)
- [Generative Adversarial U-Net for Domain-free Medical Image Augmentation](https://mlsanity.com/paper/2101.04793/) (2021)
- [An Attentive-based Generative Model for Medical Image Synthesis](https://mlsanity.com/paper/2306.01562/) (2023)
- [High-Quality Medical Image Generation from Free-hand Sketch](https://mlsanity.com/paper/2402.00353/) (2024)
- [DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation](https://mlsanity.com/paper/2501.03466/) (2025)
- [Interactive Tumor Progression Modeling via Sketch-Based Image Editing](https://mlsanity.com/paper/2503.06809/) (2025)
- [Fine-Tuning Cycle-GAN for Domain Adaptation of MRI Images](https://mlsanity.com/paper/2601.12512/) (2026)

## Abstract (arXiv)

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.