Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss.

ABSTRACT:

Background

Ultrasound imaging has been recognized as a powerful tool in clinical diagnosis. Nonetheless, the presence of speckle noise degrades the signal-to-noise of ultrasound images. Various denoising algorithms cannot fully reduce speckle noise and retain image features well for ultrasound imaging. The application of deep learning in ultrasound image denoising has attracted more and more attention in recent years.

Methods

In the article, we propose a generative adversarial network with residual dense connectivity and weighted joint loss (GAN-RW) to avoid the limitations of traditional image denoising algorithms and surpass the most advanced performance of ultrasound image denoising. The denoising network is based on U-Net architecture which includes four encoder and four

SUBMITTER: Zhang L 

PROVIDER: S-EPMC9044345 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature