It is known that changes in the mechanical properties of tissues are associated with the
onset and progression of certain diseases. Ultrasound elastography is a technique to …

In this article, two novel deep learning methods are proposed for displacement estimation in
ultrasound elastography (USE). Although convolutional neural networks (CNNs) have been …

Convolutional Neural Networks (CNN) have shown promising results for displacement
estimation in UltraSound Elastography (USE). Many modifications have been proposed to …

Deep learning is heavily being borrowed to solve problems in medical imaging applications,
and Siamese neural networks are the front runners of motion tracking. In this article, we …

Abstract 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 …

Accurate and computationally efficient motion estimation is a critical component of real-time
ultrasound strain elastography (USE). With the advent of deep-learning neural network …

The performance of ultrasound elastography (USE) heavily depends on the accuracy of
displacement estimation. Recently, convolutional neural networks (CNNs) have shown …

One of the main sources of image degradation in ultrasound imaging is the phase aberration
effect, which imposes limitations to both data acquisition and reconstruction. Phase …

In recent years, deep learning (DL) has been successfully applied to the analysis and
processing of ultrasound images. To date, most of this research has focused on …

Breast Cancer is a common type of cancer diagnosed and it is a leading cause of death
amongst the female population worldwide. Ultrasound imaging is the preferred method used …