Deep Learning in Medical Ultrasound Image Analysis: A Review

Yu Wang, Xinke Ge, He Ma, Shouliang Qi, Guanjing Zhang, Yudong Yao

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

Abstract

Ultrasound (US) is one of the most widely used imaging modalities in medical diagnosis. It has the advantages of real-time, low cost, noninvasive nature, and easy to operate. However, it also has the unique disadvantages of strong artifacts and noise and high dependence on the experience of doctors. In order to overcome the shortcomings of ultrasound diagnosis and help doctor improve the accuracy and efficiency of diagnosis, many computer aided diagnosis (CAD) systems have been developed. In recent years, deep learning has achieved great success in computer vision with its unique advantages. In the aspect of medical US image analysis, deep learning has also been exploited for itsgreat potential and more and more researchers apply it to CAD systems. In this paper, we first introduce the deep learning models commonly used in medical US image analysis; Second, we review the data preprocessing methods of medical US images, including data augmentation, denoising, and enhancement; Finally, we analyze the applications of deep learning in medical US imaging tasks (such as image classification, object detection, and image reconstruction).

Original languageEnglish
Article number9395635
Pages (from-to)54310-54324
Number of pages15
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021

Keywords

  • Deep learning
  • medical ultrasound image analysis
  • ultrasound image preprocessing

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