关键词: 超声造影图像, 卷积神经网络, 数据增强, 图像去噪

Abstract: Aiming at the noise problem of contrast-enhanced ultrasound (CEUS) image, we proposed a denoisng method of contrast-enhanced ultrasound image based on convolutional neural networks. Firstly, the number of rare contrast-enhanced ultrasound image samples was expanded by data enhancement methods such as image translation, image flipping and image rotation. Secondly, the number of samples was further expanded by overlapping small image blocks. Finally, image blocks and artificial noise were used as input training sets to train the denoising model based on convolutional network structure. The experimental results show that the proposed method can be effectively extended to different sizes of CEUS images, and the peak signal-to-noise ratio of CEUS images after denoising is higher than that of traditional image denoising methods.

Key words: contrast-enhanced ultrasound image, convolutional neural network, data enhancement, image denoising