# An Ultra-Fast Method for Simulation of Realistic Ultrasound Images

arXiv: https://arxiv.org/abs/2109.10353 · HTML: https://mlsanity.com/paper/2109.10353/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANs](https://mlsanity.com/paper/2004.00226/) (2020)
- [Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation](https://mlsanity.com/paper/2006.10166/) (2020)

## Built on by (most cited first, 1 of 1)

- [Phase Aberration Correction without Reference Data: An Adaptive Mixed Loss Deep Learning Approach](https://mlsanity.com/paper/2303.05747/) (2023)

## Abstract (arXiv)

Convolutional neural networks (CNNs) have attracted a rapidly growing interest in a variety of different processing tasks in the medical ultrasound community. However, the performance of CNNs is highly reliant on both the amount and fidelity of the training data. Therefore, scarce data is almost always a concern, particularly in the medical field, where clinical data is not easily accessible. The utilization of synthetic data is a popular approach to address this challenge. However, but simulating a large number of images using packages such as Field II is time-consuming, and the distribution of simulated images is far from that of the real images. Herein, we introduce a novel ultra-fast ultrasound image simulation method based on the Fourier transform and evaluate its performance in a lesion segmentation task. We demonstrate that data augmentation using the images generated by the proposed method substantially outperforms Field II in terms of Dice similarity coefficient, while the simulation is almost 36000 times faster (both on CPU).