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Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches
Statistics of the envelope of the backscattered radiofrequency (RF) data can be utilized to estimate several QUS parameters.
Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography
This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies.
A deep learning approach for patchless estimation of ultrasound quantitative parametric image with uncertainty measurement
Quantitative ultrasound (QUS) aims to find properties of scatterers which are related to the tissue microstructure.
Lateral Strain Imaging using Self-supervised and Physically Inspired Constraints in Unsupervised Regularized Elastography
Many modifications have been proposed to improve the displacement estimation of CNNs for USE in the axial direction.
Homodyned K-distribution: parameter estimation and uncertainty quantification using Bayesian neural networks
Speckle statistics are the QUS parameters that describe the first order statistics of ultrasound (US) envelope data.
Infusing known operators in convolutional neural networks for lateral strain imaging in ultrasound elastography
This method took into account the range of the feasible lateral strain defined by the rules of physics of motion and employed a regularization strategy to improve the lateral strains.
Deep Estimation of Speckle Statistics Parametric Images
Quantitative Ultrasound (QUS) provides important information about the tissue properties.
Physically Inspired Constraint for Unsupervised Regularized Ultrasound Elastography
Recently, the architecture of the optical flow networks has been modified to be able to use RF data.
Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation
We also show that the network fine-tuned by our proposed method using experimental phantom data performs well on in vivo data similar to the network fine-tuned on in vivo data.
Robust Scatterer Number Density Segmentation of Ultrasound Images
In conventional methods, the envelope data is divided into small overlapping windows (a strategy here we refer to as patching), and statistical parameters such as SNR and skewness are employed to classify each patch of envelope data.
Ultrasound Domain Adaptation Using Frequency Domain Analysis
A common issue in exploiting simulated ultrasound data for training neural networks is the domain shift problem, where the trained models on synthetic data are not generalizable to clinical data.
Ultrasound Scatterer Density Classification Using Convolutional Neural Networks by Exploiting Patch Statistics
The results also show that the proposed network is able to work with different imaging parameters with no need for a reference phantom.
Semi-Supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastography
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.
Image and Video Processing
