# Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images

arXiv: https://arxiv.org/abs/2103.05745 · HTML: https://mlsanity.com/paper/2103.05745/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Toward Multimodal Image-to-Image Translation](https://mlsanity.com/paper/1711.11586/) (2017)
- [Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data](https://mlsanity.com/paper/1802.10151/) (2018)
- [Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation](https://mlsanity.com/paper/2103.03781/) (2021)
- [Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis](https://mlsanity.com/paper/2008.08698/) (2020)
- [Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning](https://mlsanity.com/paper/1712.07881/) (2017)
- [Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation](https://mlsanity.com/paper/2006.10166/) (2020)
- [Deep Image Translation for Enhancing Simulated Ultrasound Images](https://mlsanity.com/paper/2006.10850/) (2020)
- [Learning Ultrasound Rendering from Cross-Sectional Model Slices for Simulated Training](https://mlsanity.com/paper/2101.08339/) (2021)

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

- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis](https://mlsanity.com/paper/2303.12644/) (2023)
- [D'ARTAGNAN: Counterfactual Video Generation](https://mlsanity.com/paper/2206.01651/) (2022)
- [EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing](https://mlsanity.com/paper/2406.00808/) (2024)
- [Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2110.02117/) (2021)
- [Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesis](https://mlsanity.com/paper/2408.03035/) (2024)
- [EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation](https://mlsanity.com/paper/2503.22357/) (2025)
- [LOTUS: Learning to Optimize Task-based US representations](https://mlsanity.com/paper/2307.16021/) (2023)
- [Cardiac ultrasound simulation for autonomous ultrasound navigation](https://mlsanity.com/paper/2402.06463/) (2024)
- [Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations](https://mlsanity.com/paper/2302.10698/) (2023)
- [Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss](https://mlsanity.com/paper/2311.00412/) (2023)
- [Label-free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesis](https://mlsanity.com/paper/2512.09418/) (2025)
- [InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiography](https://mlsanity.com/paper/2512.09422/) (2025)
- [Abdominal Ultrasound Simulation from Semantic Labels using Paired Label-to-Physics-Based Image Translation](https://mlsanity.com/paper/2610.08849/) (2026)

## Abstract (arXiv)

Interactive simulation of ultrasound imaging greatly facilitates sonography training. Although ray-tracing based methods have shown promising results, obtaining realistic images requires substantial modeling effort and manual parameter tuning. In addition, current techniques still result in a significant appearance gap between simulated images and real clinical scans. Herein we introduce a novel content-preserving image translation framework (ConPres) to bridge this appearance gap, while maintaining the simulated anatomical layout. We achieve this goal by leveraging both simulated images with semantic segmentations and unpaired in-vivo ultrasound scans. Our framework is based on recent contrastive unpaired translation techniques and we propose a regularization approach by learning an auxiliary segmentation-to-real image translation task, which encourages the disentanglement of content and style. In addition, we extend the generator to be class-conditional, which enables the incorporation of additional losses, in particular a cyclic consistency loss, to further improve the translation quality. Qualitative and quantitative comparisons against state-of-the-art unpaired translation methods demonstrate the superiority of our proposed framework.