Ultrasound Domain Adaptation Using Frequency Domain Analysis

TL;DR

This paper applies Fourier Domain Adaptation to ultrasound imaging, replacing low-frequency spectral components of synthetic data with real data to improve neural network generalization, especially for breast lesion segmentation.

Contribution

It introduces a novel application of FDA in ultrasound imaging to address domain shift caused by tissue attenuation and SOS variations.

Findings

3.5% higher Dice coefficient in segmentation accuracy

Effective reduction of domain shift between synthetic and real ultrasound data

Demonstrated improvement in breast lesion segmentation performance

Abstract

A common issue in exploiting simulated ultrasound data for training neural networks is the domain shift problem, where the trained models on synthetic data are not generalizable to clinical data. Recently, Fourier Domain Adaptation (FDA) has been proposed in the field of computer vision to tackle the domain shift problem by replacing the magnitude of the low-frequency spectrum of a synthetic sample (source) with a real sample (target). This method is attractive in ultrasound imaging given that two important differences between synthetic and real ultrasound data are caused by unknown values of attenuation and speed of sound (SOS) in real tissues. Attenuation leads to slow variations in the amplitude of the B-mode image, and SOS mismatch creates aberration and subsequent blurring. As such, both domain shifts cause differences in the low-frequency components of the envelope data, which are replaced in the proposed method. We demonstrate that applying the FDA method to the synthetic data, simulated by Field II, obtains an 3.5\% higher Dice similarity coefficient for a breast lesion segmentation task.