  Convolutional Neural Networks (CNN) have been found to have great potential
in optical flow problems thanks to an abundance of data available for training
a deep network. The displacement estimation step in UltraSound Elastography
(USE) can be viewed as an optical flow problem. Despite the high performance of
CNNs in optical flow, they have been rarely used for USE due to unique
challenges that both input and output of USE networks impose. Ultrasound data
has much higher high-frequency content compared to natural images. The outputs
are also drastically different, where displacement values in USE are often
smooth without sharp motions or discontinuities. The general trend is currently
to use pre-trained networks and fine-tune them on a small simulation ultrasound
database. However, realistic ultrasound simulation is computationally
expensive. Also, the simulation techniques do not model complex motions,
nonlinear and frequency-dependent acoustics, and many sources of artifact in
ultrasound imaging. Herein, we propose an unsupervised fine-tuning technique
which enables us to employ a large unlabeled dataset for fine-tuning of a CNN
optical flow network. We show that the proposed unsupervised fine-tuning method
substantially improves the performance of the network and reduces the artifacts
generated by networks trained on computer vision databases.
