Abstract
Recently, Convolutional Neural Networks (CNNs) have been very successful in optical flow estimation in computer vision. UltraSound Elastography (USE) displacement estimation step can be performed by optical flow CNNs. However, there is a large domain gap between ultrasound Radio-Frequency (RF) data and computer vision images which reduces the overall accuracy of displacement estimation. Some modifications of the network architecture are required to be able to extract reliable information from RF data. Modified Pyramidal Network (MPWC-Net) which is based on the well-known PWC-Net was among the first attempts that adopts the optical flow CNNs to USE displacement estimation. However, MPWC-Net suffers from several shortcomings that limit its application especially for unsupervised training. In this paper, we propose additional modifications to substantially improve MPWC-Net. We also publicly released the network’s trained weights.
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