Deep Learning in Medical Ultrasound Image Analysis: A Review

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

doi:10.1109/access.2021.3071301

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 its great 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).

Highlights

Ultrasound (US) is an important part of medical imaging and one of the most commonly used medical diagnostic techniques
The results show that CFS-fully convolutional networks (FCN) is significantly better than other deep learning methods in segmentation performance
Cadieu et al [108] confirmed in a study that deep learning can achieve the same performance as the primate visual inferotemporal cortex, which explains that deep learning has advantages over traditional methods in computer vision tasks

Summary

Introduction

Ultrasound (US) is an important part of medical imaging and one of the most commonly used medical diagnostic techniques. MEDICAL ULTRASOUND IMAGE PREPROCESSING One of the reasons for the great success of deep learning in various fields is the support of a large number of labeled training samples so that the neural network can obtain good learning performance. Relying only on small sample datasets, it is difficult to achieve satisfactory performance of deep learning in medical image analysis.

Results

Conclusion