activity
20182022
most citedSuper-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning

31 citations · 40 across the 8 of their papers we have counts for

collaborators

19 papers

physics.med-ph202231 cited

Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning

Nathan Blanken, Jelmer M. Wolterink, Hervé Delingette +3

Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles i…

cs.CV2021

Bayesian Logistic Shape Model Inference: application to cochlea image segmentation

Wang Zihao, Demarcy Thomas, Vandersteen Clair +4

Incorporating shape information is essential for the delineation of many organs and anatomical structures in medical images. While previous work has mainly focused on parametric sp…

cs.CV2021

Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks

Wang Zihao, Vandersteen Clair, Demarcy Thomas +4

Metal Artifacts creates often difficulties for a high quality visual assessment of post-operative imaging in {c}omputed {t}omography (CT). A vast body of methods have been proposed…

eess.IV20212 cited

Combining Bayesian and Deep Learning Methods for the Delineation of the Fan in Ultrasound Images

Hind Dadoun, Hervé Delingette, Anne-Laure Rousseau +2

Ultrasound (US) images usually contain identifying information outside the ultrasound fan area and manual annotations placed by the sonographers during exams. For those images to b…

cs.CV20201 cited

Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix

Julian Krebs, Hervé Delingette, Nicholas Ayache +1

We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the…

eess.SP2020

Long Short-Term Memory Neuron Equalizer

Zihao Wang, Zhifei Xu, Jiayi He +3

In this work we propose a neuromorphic hardware based signal equalizer by based on the deep learning implementation. The proposed neural equalizer is plasticity trainable equalizer…