Speckle noise reduction for medical ultrasound images based on cycle-consistent generative adversarial network

Medical ultrasound (US) images are corrupted by speckle noise, which can adversely affect the disease diagnosis and treatment. Recently, the cycle-consistent adversarial network (CycleGAN) has been used in the style transfer for both natural and medical images. In this study, we aim to develop a US despeckling method based on the CycleGAN by the style transfer between the speckled noisy data domain and noise-free data domain by forming a bi-directional universal mapping. The inputs of noisy and noise-free images are designed in the CycleGAN model. For simulation work, we use both the multiplicative model and the spatial impulse response model to obtain noisy images from noise-free images. However, noise-free US images are not clinically available. Hence, for the real US despeckling scenario, the clinical images of hearts, lymph nodes, and breast tumors are used as noisy images; and the high-quality images that are derived from the clinical images by despeckling with the Gaborbased anisotropic diffusion (GAD) and selected with a new metric named the edge-to-noise ratio, are used as the noise-free images. We compare our CycleGAN based denoising method with nine existing denoising methods, i.e., the speckle reduction anisotropic diffusion, GAD, non-local means, wavelet transform, unbiased non-local means, statistical nearest-neighbors, TVHTVM, improved non-local self-similarity measures, and generative adversarial network. Our method outperforms other methods by visual assessment and quantitative comparison, which demonstrates its superiority for noise reduction and detail preservation.

Automated summary

In this study, a US despeckling method based on the CycleGAN is developed to reduce noise in medical ultrasound images, improving disease diagnosis and treatment.