An Ultra-Fast Method for Simulation of Realistic Ultrasound Images
This addresses data scarcity in medical ultrasound for researchers and practitioners, though it is incremental as it builds on synthetic data approaches.
The paper tackled the problem of slow and unrealistic ultrasound image simulation for training CNNs in medical tasks by introducing an ultra-fast Fourier transform-based method, which achieved a 36000x speedup on CPU and improved lesion segmentation Dice scores compared to existing methods.
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).