  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.
