A Deep Learning Based Alternative to Beamforming Ultrasound Images

Abstract

Deep learning methods are capable of performing sophisticated tasks when applied to a myriad of artificial intelligent (AI) research fields. In this paper, we introduce a novel approach to replace the inherently flawed beamforming step during ultrasound image formation by applying deep learning directly to RF channel data. Specifically, we pose the ultrasound beamforming process as a segmentation problem and apply a fully convolutional neural network architecture to segment anechoic cysts from surrounding tissue. We train our network on a dataset created using the Field II ultrasound simulation software to simulate plane wave imaging with a single insonification angle. We demonstrate the success of our architecture in extracting tissue information directly from the raw channel data, which completely bypasses the beamforming step that would otherwise require multiple insonification angles for plane wave imaging. Our simulated results produce mean Dice coefficient of 0.98 ± 0.02, when measuring the overlap between ground truth cyst locations and cyst locations determined by the network. The proposed approach is promising for developing dedicated deep-learning networks to improve the real-time ultrasound image formation process.