  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).
