Controllable Deep Learning Denoising Model for Ultrasound Images Using Synthetic Noisy Image

Mingfu Jiang, Chenzhi You, Mingwei Wang, Heye Zhang, Zhifan Gao, Dawei Wu, Tao Tan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Medical ultrasound imaging has gained widespread prevalence in human muscle and internal organ diagnosis. Nevertheless, various factors such as the interference effect of ultrasonic echoes, mutual interference between scattered beams, inhomogeneity and uncertainty in the spatial distribution of human body tissue, inappropriate operation, and imaging signal transmission processes, can lead to noise and distortion in ultrasound images. These factors make it difficult to obtain clean and accurate ultrasound images, which may adversely affect medical diagnosis and treatment processes. While traditional denoising methods are time-consuming, they are also not effective in removing speckle noise while retaining image details, leading to potential misdiagnosis. Therefore, there is a significant need to accurately and quickly denoise medical ultrasound images to enhance image quality. In this paper, we propose a flexible and lightweight deep learning denoising method for ultrasound images. Initially, we utilize a considerable number of natural images to train the convolutional neural network for acquiring a pre-trained denoising model. Next, we employ the plane-wave imaging technique to generate simulated noisy ultrasound images for further transfer learning of the pre-trained model. As a result, we obtain a non-blind, lightweight, fast, and accurate denoiser. Experimental results demonstrate the superiority of our proposed method in terms of denoising speed, flexibility, and effectiveness compared to conventional convolutional neural network denoisers for ultrasound images.

Original languageEnglish
Title of host publicationAdvances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings
EditorsBin Sheng, Lei Bi, Jinman Kim, Nadia Magnenat-Thalmann, Daniel Thalmann
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031500688
Publication statusPublished - 2024
Event40th Computer Graphics International Conference, CGI 2023 - Shanghai, China
Duration: 28 Aug 20231 Sept 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference40th Computer Graphics International Conference, CGI 2023


  • Lightweight Model
  • Noise Transfer Learning
  • Non-blind Ultrasound Image Denoising
  • Plane-wave Imaging


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