MPWC-Net++: evolution of optical flow pyramidal convolutional neural network for ultrasound elastography

Ali K. Z. Tehrani; Hassan Rivaz

doi:10.1117/12.2582316

15 February 2021 MPWC-Net++: evolution of optical flow pyramidal convolutional neural network for ultrasound elastography

Ali K. Z. Tehrani, Hassan Rivaz

Author Affiliations +

Proceedings Volume 11602, Medical Imaging 2021: Ultrasonic Imaging and Tomography; 1160206 (2021) https://doi.org/10.1117/12.2582316
Event: SPIE Medical Imaging, 2021, Online Only

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.

Conference Presentation

Citation Download Citation

Ali K. Z. Tehrani and Hassan Rivaz "MPWC-Net++: evolution of optical flow pyramidal convolutional neural network for ultrasound elastography", Proc. SPIE 11602, Medical Imaging 2021: Ultrasonic Imaging and Tomography, 1160206 (15 February 2021); https://doi.org/10.1117/12.2582316

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