TY - JOUR
T1 - Deep Learning in Medical Ultrasound Image Analysis
T2 - A Review
AU - Wang, Yu
AU - Ge, Xinke
AU - Ma, He
AU - Qi, Shouliang
AU - Zhang, Guanjing
AU - Yao, Yudong
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - 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).
AB - 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).
KW - Deep learning
KW - medical ultrasound image analysis
KW - ultrasound image preprocessing
UR - http://www.scopus.com/inward/record.url?scp=85103913858&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103913858&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3071301
DO - 10.1109/ACCESS.2021.3071301
M3 - Article
AN - SCOPUS:85103913858
VL - 9
SP - 54310
EP - 54324
JO - IEEE Access
JF - IEEE Access
M1 - 9395635
ER -