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- Eindhoven University of TechnologyNL56 papers
- Delft University of TechnologyNL21 papers
- Leiden UniversityNL20 papers
- Signify (Netherlands)NL18 papers
- Institute for Atomic and Molecular PhysicsNL14 papers
- NXP (Netherlands)NL13 papers
- Philips (Finland)FI9 papers
- Utrecht UniversityNL8 papers
- Max Planck Institute for Solid State ResearchDE7 papers
- Philips (Germany)DE6 papers
- Radboud University NijmegenNL6 papers
- RWTH Aachen UniversityDE5 papers
13 papers · 1 filter
Physics-informed neural networks for myocardial perfusion MRI quantification
Rudolf L. M. van Herten, Amedeo Chiribiri, Marcel Breeuwer +2
Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data…
X-ray verification of sol-gel resist shrinkage in substrate-conformal imprint lithography for a replicated blazed reflection grating
Jake A. McCoy, Marc A. Verschuuren, Drew M. Miles +1
Surface-relief gratings fabricated through nanoimprint lithography (NIL) are prone to topographic distortion induced by resist shrinkage. Characterizing the impact of this effect o…
Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound
Hongxu Yang, Caifeng Shan, Alexander F. Kolen +1
Accurate and efficient catheter segmentation in 3D ultrasound (US) is essential for cardiac intervention. Currently, the state-of-the-art segmentation algorithms are based on convo…
XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms
Sina Amirrajab, Samaneh Abbasi-Sureshjani, Yasmina Al Khalil +4
Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with…
Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet
Hongxu Yang, Caifeng Shan, Alexander F. Kolen +1
Catheter segmentation in 3D ultrasound is important for computer-assisted cardiac intervention. However, a large amount of labeled images are required to train a successful deep co…
Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders
Nina Shvetsova, Bart Bakker, Irina Fedulova +2
Anomaly detection is the problem of recognizing abnormal inputs based on the seen examples of normal data. Despite recent advances of deep learning in recognizing image anomalies,…