# Learning Ultrasound Rendering from Cross-Sectional Model Slices for Simulated Training

arXiv: https://arxiv.org/abs/2101.08339 · HTML: https://mlsanity.com/paper/2101.08339/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 20 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [A Survey of FPGA-Based Neural Network Accelerator](https://mlsanity.com/paper/1712.08934/) (2017)
- [Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks](https://mlsanity.com/paper/1707.05392/) (2017)
- [Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning](https://mlsanity.com/paper/1712.07881/) (2017)
- [Deep Image Translation for Enhancing Simulated Ultrasound Images](https://mlsanity.com/paper/2006.10850/) (2020)
- [Deep Generative Models to Simulate 2D Patient-Specific Ultrasound Images in Real Time](https://mlsanity.com/paper/2005.04931/) (2020)

## Built on by (most cited first, 3 of 3)

- [Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images](https://mlsanity.com/paper/2103.05745/) (2021)
- [Lean Unet: A Compact Model for Image Segmentation](https://mlsanity.com/paper/2512.03834/) (2025)
- [Abdominal Ultrasound Simulation from Semantic Labels using Paired Label-to-Physics-Based Image Translation](https://mlsanity.com/paper/2610.08849/) (2026)

## Abstract (arXiv)

Purpose. Given the high level of expertise required for navigation and interpretation of ultrasound images, computational simulations can facilitate the training of such skills in virtual reality. With ray-tracing based simulations, realistic ultrasound images can be generated. However, due to computational constraints for interactivity, image quality typically needs to be compromised. Methods. We propose herein to bypass any rendering and simulation process at interactive time, by conducting such simulations during a non-time-critical offline stage and then learning image translation from cross-sectional model slices to such simulated frames. We use a generative adversarial framework with a dedicated generator architecture and input feeding scheme, which both substantially improve image quality without increase in network parameters. Integral attenuation maps derived from cross-sectional model slices, texture-friendly strided convolutions, providing stochastic noise and input maps to intermediate layers in order to preserve locality are all shown herein to greatly facilitate such translation task. Results. Given several quality metrics, the proposed method with only tissue maps as input is shown to provide comparable or superior results to a state-of-the-art that uses additional images of low-quality ultrasound renderings. An extensive ablation study shows the need and benefits from the individual contributions utilized in this work, based on qualitative examples and quantitative ultrasound similarity metrics. To that end, a local histogram statistics based error metric is proposed and demonstrated for visualization of local dissimilarities between ultrasound images.