  Displacement estimation is very important in ultrasound elastography and
failing to estimate displacement correctly results in failure in generating
strain images. As conventional ultrasound elastography techniques suffer from
decorrelation noise, they are prone to fail in estimating displacement between
echo signals obtained during tissue distortions. This study proposes a novel
elastography technique which addresses the decorrelation in estimating
displacement field. We call our method GLUENet (GLobal Ultrasound Elastography
Network) which uses deep Convolutional Neural Network (CNN) to get a coarse
time-delay estimation between two ultrasound images. This displacement is later
used for formulating a nonlinear cost function which incorporates similarity of
RF data intensity and prior information of estimated displacement. By
optimizing this cost function, we calculate the finer displacement by
exploiting all the information of all the samples of RF data simultaneously.
The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) of the strain
images from our technique is very much close to that of strain images from
GLUE. While most elastography algorithms are sensitive to parameter tuning, our
robust algorithm is substantially less sensitive to parameter tuning.
