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
12 papers · 1 filter
The 4D Human Embryonic Brain Atlas: spatiotemporal atlas generation for rapid anatomical changes
Wietske A. P. Bastiaansen, Melek Rousian, Anton H. J. Koning +4
Early brain development is crucial for lifelong neurodevelopmental health. However, current clinical practice offers limited knowledge of normal embryonic brain anatomy on ultrasou…
Predicting Age from White Matter Diffusivity with Residual Learning
Chenyu Gao, Michael E. Kim, Ho Hin Lee +17
Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimat…
TabAttention: Learning Attention Conditionally on Tabular Data
Michal K. Grzeszczyk, Szymon Płotka, Beata Rebizant +6
Medical data analysis often combines both imaging and tabular data processing using machine learning algorithms. While previous studies have investigated the impact of attention me…
Robust deformable image registration using cycle-consistent implicit representations
Louis D. van Harten, Jaap Stoker, Ivana Išgum
Recent works in medical image registration have proposed the use of Implicit Neural Representations, demonstrating performance that rivals state-of-the-art learning-based methods.…
Modeling Barrett's Esophagus Progression using Geometric Variational Autoencoders
Vivien van Veldhuizen, Sharvaree Vadgama, Onno J. de Boer +2
Early detection of Barrett's Esophagus (BE), the only known precursor to Esophageal adenocarcinoma (EAC), is crucial for effectively preventing and treating esophageal cancer. In t…
Preoperative brain tumor imaging: models and software for segmentation and standardized reporting
D. Bouget, A. Pedersen, A. S. Jakola +27
For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the l…
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography
Roy Schwartz, Hagar Khalid, Sandra Liakopoulos +12
Purpose - To develop and validate a deep learning (DL) framework for the detection and quantification of drusen and reticular pseudodrusen (RPD) on optical coherence tomography sca…
Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI
Jörg Sander, Bob D. de Vos, Ivana Išgum
High-resolution medical images are beneficial for analysis but their acquisition may not always be feasible. Alternatively, high-resolution images can be created from low-resolutio…
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction
D. Karkalousos, S. Noteboom, H. E. Hulst +2
Machine Learning methods can learn how to reconstruct Magnetic Resonance Images and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physi…
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…
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…