4 papers
Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation
Ali K. Z. Tehrani, Mostafa Sharifzadeh, Emad Boctor +1
The performance of ultrasound elastography (USE) heavily depends on the accuracy of displacement estimation. Recently, Convolutional Neural Networks (CNN) have shown promising perf…
Robust Scatterer Number Density Segmentation of Ultrasound Images
Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hassan Rivaz
Quantitative UltraSound (QUS) aims to reveal information about the tissue microstructure using backscattered echo signals from clinical scanners. Among different QUS parameters, sc…
Ultrasound Domain Adaptation Using Frequency Domain Analysis
Mostafa Sharifzadeh, Ali K. Z. Tehrani, Habib Benali +1
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…
Semi-Supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastography
Ali K. Z. Tehrani, Morteza Mirzaei, Hassan Rivaz
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. The displ…