# Deep Image Translation for Enhancing Simulated Ultrasound Images

arXiv: https://arxiv.org/abs/2006.10850 · HTML: https://mlsanity.com/paper/2006.10850/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis](https://mlsanity.com/paper/2201.12260/) (2022)
- [Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images](https://mlsanity.com/paper/2103.05745/) (2021)
- [Learning Ultrasound Rendering from Cross-Sectional Model Slices for Simulated Training](https://mlsanity.com/paper/2101.08339/) (2021)
- [Abdominal Ultrasound Simulation from Semantic Labels using Paired Label-to-Physics-Based Image Translation](https://mlsanity.com/paper/2610.08849/) (2026)

## Abstract (arXiv)

Ultrasound simulation based on ray tracing enables the synthesis of highly realistic images. It can provide an interactive environment for training sonographers as an educational tool. However, due to high computational demand, there is a trade-off between image quality and interactivity, potentially leading to sub-optimal results at interactive rates. In this work we introduce a deep learning approach based on adversarial training that mitigates this trade-off by improving the quality of simulated images with constant computation time. An image-to-image translation framework is utilized to translate low quality images into high quality versions. To incorporate anatomical information potentially lost in low quality images, we additionally provide segmentation maps to image translation. Furthermore, we propose to leverage information from acoustic attenuation maps to better preserve acoustic shadows and directional artifacts, an invaluable feature for ultrasound image interpretation. The proposed method yields an improvement of 7.2% in Fr'echet Inception Distance and 8.9% in patch-based Kullback-Leibler divergence.