336 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of AmsterdamNL35 papers
- Vrije Universiteit AmsterdamNL22 papers
- Amsterdam NeuroscienceNL12 papers
- University College LondonGB10 papers
- Delft University of TechnologyNL9 papers
- Leiden University Medical CenterNL9 papers
- Erasmus MCNL8 papers
- Radboud University NijmegenNL8 papers
- University Medical Center UtrechtNL8 papers
- Leiden UniversityNL7 papers
- Erasmus University RotterdamNL5 papers
- KU LeuvenBE5 papers
11 papers · 1 filter
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
Matthew J. Muckley, Bruno Riemenschneider, Alireza Radmanesh +20
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reco…
rags2ridges: A One-Stop-Shop for Graphical Modeling of High-Dimensional Precision Matrices
Carel F. W. Peeters, Anders Ellern Bilgrau, Wessel N. van Wieringen
A graphical model is an undirected network representing the conditional independence properties between random variables. Graphical modeling has become part and parcel of systems o…
A one-dimensional morphoelastic model for burn injuries: stability analysis, numerical validation and biological interpretation
Ginger Egberts, Fred Vermolen, Paul van Zuijlen
To deal with permanent deformations and residual stresses, we consider a morphoelastic model for the scar formation as the result of wound healing after a skin trauma. Next to the…
Deep Learning from Dual-Energy Information for Whole-Heart Segmentation in Dual-Energy and Single-Energy Non-Contrast-Enhanced Cardiac CT
Steffen Bruns, Jelmer M. Wolterink, Richard A. P. Takx +5
Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic…
Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images
Julia M. H. Noothout, Bob D. de Vos, Jelmer M. Wolterink +6
In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images. We employ a global-to-local localization approach using fully…
Complex networks with tuneable dimensions as a universality playground
Ana P. Millán, Giacomo Gori, Federico Battiston +2
Universality is one of the key concepts in understanding critical phenomena. However, for interacting inhomogeneous systems described by complex networks a clear understanding of t…
Towards defining reference materials for extracellular vesicle size, concentration, refractive index and epitope abundance
Joshua A. Welsh, Edwin van der Pol, Britta A. Bettin +11
Accurate characterization of extracellular vesicles (EVs) is critical to explore their diagnostic and therapeutic applications. As the EV research field has developed, so too have…
Using smartphones and wearable devices to monitor behavioural changes during COVID-19
Shaoxiong Sun, Amos Folarin, Yatharth Ranjan +23
We aimed to explore the utility of the recently developed open-source mobile health platform RADAR-base as a toolbox to rapidly test the effect and response to NPIs aimed at limiti…
The test-negative design with additional population controls: a practical approach to rapidly obtain information on the causes of the SARS-CoV-2 epidemic
Jan P Vandenbroucke, Elizabeth B Brickley, Christina M J E Vandenbroucke-Grauls +1
Testing of symptomatic persons for infection with SARS-CoV-2 is occurring worldwide. We propose two types of case-control studies that can be carried out jointly in test-settings f…
A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study
Ahmed El-Gazzar, Mirjam Quaak, Leonardo Cerliani +3
Functional Magnetic Resonance Imaging (fMRI) captures the temporal dynamics of neural activity as a function of spatial location in the brain. Thus, fMRI scans are represented as 4…
Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy
M. Virgolin, Z. Wang, B. V. Balgobind +11
To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent…