SweiNet is an uncertainty-quantifying shear wave speed (SWS) estimator for ultrasound shear wave elasticity (SWE) imaging.

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Deep Learning swei-net
Overview

SweiNet

SweiNet is an uncertainty-quantifying shear wave speed (SWS) estimator for ultrasound shear wave elasticity (SWE) imaging.

SweiNet takes as input a 2D space-by-time array of tracked particle motion. It outputs the estimated SWS and estimated uncertainty, both in units of meters per second.

SweiNet was originally trained on a large dataset of in vivo cervix SWE acquisitions. The predicted uncertainty is well-calibrated to these data. However, with a few pre-processing steps, SweiNet can easily be applied to other datasets. See the example notebook: example.ipynb.

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Releases(v1.0.0)

  • v1.0.0(Mar 22, 2022)

    SweiNet is an uncertainty-quantifying shear wave speed estimator for ultrasound shear wave elasticity imaging. This is the initial release of the code and trained model for SweiNet.

    Source code(tar.gz)
    Source code(zip)
Owner
Felix Jin
MD/PhD Candidate at Duke University
Felix Jin
GitHub
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