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Combining physics-based modeling and deep learning for ultrasound elastography
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Ultrasound elasticity images which enable the visualization of quantitative maps of tissue stiffness can be reconstructed by solving an inverse problem. Classical model-based appro…
Regularization by Adversarial Learning for Ultrasound Elasticity Imaging
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Classical model-based imaging methods for ultrasound elasticity inverse problem require prior constraints about the underlying elasticity patterns, while finding the appropriate ha…
Finite Element Reconstruction Of Stiffness Images In MR Elastography Using Statistical Physical Forward Modeling And Proximal Optimization Methods
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Quantitative characterization of tissue properties, known as elasticity imaging, can be cast as solving an ill-posed inverse problem. The finite element methods (FEMs) in magnetic…
Depth extraction from a single compressive hologram
Baturay Ozgurun, Mujdat Cetin
We propose a novel method that records a single compressive hologram in a short time and extracts the depth of a scene from that hologram using a stereo disparity technique. The me…
Combining nonparametric spatial context priors with nonparametric shape priors for dendritic spine segmentation in 2-photon microscopy images
Ertunc Erdil, Ali Ozgur Argunsah, Tolga Tasdizen +2
Data driven segmentation is an important initial step of shape prior-based segmentation methods since it is assumed that the data term brings a curve to a plausible level so that s…