122 citations · 193 across the 12 of their papers we have counts for
31 papers
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…