# Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANs

arXiv: https://arxiv.org/abs/2004.00226 · HTML: https://mlsanity.com/paper/2004.00226/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis](https://mlsanity.com/paper/2204.06929/) (2022)
- [Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Models](https://mlsanity.com/paper/2503.14966/) (2025)
- [Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decoupling](https://mlsanity.com/paper/2207.00474/) (2022)
- [An Ultra-Fast Method for Simulation of Realistic Ultrasound Images](https://mlsanity.com/paper/2109.10353/) (2021)
- [Interactive Tumor Progression Modeling via Sketch-Based Image Editing](https://mlsanity.com/paper/2503.06809/) (2025)
- [Real, fake and synthetic faces -- does the coin have three sides?](https://mlsanity.com/paper/2404.01878/) (2024)

## Abstract (arXiv)

Ultrasound (US) is widely accepted in clinic for anatomical structure inspection. However, lacking in resources to practice US scan, novices often struggle to learn the operation skills. Also, in the deep learning era, automated US image analysis is limited by the lack of annotated samples. Efficiently synthesizing realistic, editable and high resolution US images can solve the problems. The task is challenging and previous methods can only partially complete it. In this paper, we devise a new framework for US image synthesis. Particularly, we firstly adopt a sketch generative adversarial networks (Sgan) to introduce background sketch upon object mask in a conditioned generative adversarial network. With enriched sketch cues, Sgan can generate realistic US images with editable and fine-grained structure details. Although effective, Sgan is hard to generate high resolution US images. To achieve this, we further implant the Sgan into a progressive growing scheme (PGSgan). By smoothly growing both generator and discriminator, PGSgan can gradually synthesize US images from low to high resolution. By synthesizing ovary and follicle US images, our extensive perceptual evaluation, user study and segmentation results prove the promising efficacy and efficiency of the proposed PGSgan.