843 citations · 4.4k across the 41 of their papers we have counts for
17 papers · 1 filter
Deep Learning-Based Prediction of Fractional Flow Reserve along the Coronary Artery
Nils Hampe, Sanne G. M. van Velzen, Jean-Paul Aben +2
Functionally significant coronary artery disease (CAD) is caused by plaque buildup in the coronary arteries, potentially leading to narrowing of the arterial lumen, i.e. coronary s…
Generative Models for Reproducible Coronary Calcium Scoring
Sanne G. M. van Velzen, Bob D. de Vos, Julia M. H. Noothout +3
Purpose: Coronary artery calcium (CAC) score, i.e. the amount of CAC quantified in CT, is a strong and independent predictor of coronary heart disease (CHD) events. However, CAC sc…
Automatic segmentation with detection of local segmentation failures in cardiac MRI
Jörg Sander, Bob D. de Vos, Ivana Išgum
Segmentation of cardiac anatomical structures in cardiac magnetic resonance images (CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases. To inc…
Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples
Jörg Sander, Bob D. de Vos, Ivana Išgum
Although high resolution isotropic 3D medical images are desired in clinical practice, their acquisition is not always feasible. Instead, lower resolution images are upsampled to h…
Deep Group-wise Variational Diffeomorphic Image Registration
Tycho F. A. van der Ouderaa, Ivana Išgum, Wouter B. Veldhuis +1
Deep neural networks are increasingly used for pair-wise image registration. We propose to extend current learning-based image registration to allow simultaneous registration of mu…
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…
Automatic Online Quality Control of Synthetic CTs
Louis D. van Harten, Jelmer M. Wolterink, Joost J. C. Verhoeff +1
Accurate MR-to-CT synthesis is a requirement for MR-only workflows in radiotherapy (RT) treatment planning. In recent years, deep learning-based approaches have shown impressive re…
Combined analysis of coronary arteries and the left ventricular myocardium in cardiac CT angiography for detection of patients with functionally significant stenosis
Majd Zreik, Tim Leiner, Nadieh Khalili +5
Treatment of patients with obstructive coronary artery disease is guided by the functional significance of a coronary artery stenosis. Fractional flow reserve (FFR), measured durin…
3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Artificial Deformations
Hessam Sokooti, Bob de Vos, Floris Berendsen +5
We propose a supervised nonrigid image registration method, trained using artificial displacement vector fields (DVF), for which we propose and compare three network architectures.…
CNN-Based Segmentation of the Cardiac Chambers and Great Vessels in Non-Contrast-Enhanced Cardiac CT
Steffen Bruns, Jelmer M. Wolterink, Robbert W. van Hamersvelt +2
Quantification of cardiac structures in non-contrast CT (NCCT) could improve cardiovascular risk stratification. However, setting a manual reference to train a fully convolutional…
Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography
Jelmer M. Wolterink, Tim Leiner, Ivana Išgum
Detection of coronary artery stenosis in coronary CT angiography (CCTA) requires highly personalized surface meshes enclosing the coronary lumen. In this work, we propose to use gr…
Vertebra partitioning with thin-plate spline surfaces steered by a convolutional neural network
Nikolas Lessmann, Jelmer M. Wolterink, Majd Zreik +3
Thin-plate splines can be used for interpolation of image values, but can also be used to represent a smooth surface, such as the boundary between two structures. We present a meth…
Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy
Mohamed S. Elmahdy, Jelmer M. Wolterink, Hessam Sokooti +2
Joint image registration and segmentation has long been an active area of research in medical imaging. Here, we reformulate this problem in a deep learning setting using adversaria…
Automatic brain tissue segmentation in fetal MRI using convolutional neural networks
N. Khalili, N. Lessmann, E. Turk +6
MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segment…
Generative adversarial network for segmentation of motion affected neonatal brain MRI
N. Khalili, E. Turk, M. Zreik +3
Automatic neonatal brain tissue segmentation in preterm born infants is a prerequisite for evaluation of brain development. However, automatic segmentation is often hampered by mot…
Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography
Majd Zreik, Robbert W. van Hamersvelt, Nadieh Khalili +5
In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically establi…