Deep Learning in Ultrasound Elastography Imaging
It addresses the problem of improving tissue stiffness characterization for medical diagnosis, but it is incremental as it reviews existing methods rather than introducing new ones.
This review paper examines the application of deep learning techniques, such as multilayer perceptrons, convolutional neural networks, and recurrent neural networks, to ultrasound elastography for characterizing tissue stiffness in disease diagnosis, highlighting recent advances in algorithm development and clinical applications.
It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayer perceptron, convolutional neural network, and recurrent neural network are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected.