333 citations · 1.6k across the 20 of their papers we have counts for
20 papers
From algorithms to action: improving patient care requires causality
Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J. C. Verhoeff +2
In cancer research there is much interest in building and validating outcome predicting outcomes to support treatment decisions. However, because most outcome prediction models are…
AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis
Yutong Chen, Carola-Bibiane Schönlieb, Pietro Liò +6
Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence, deep n…
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…
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…
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…
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…
Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT
Bob D. de Vos, Jelmer M. Wolterink, Tim Leiner +3
Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet demands of the increa…
Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier
Jelmer M. Wolterink, Robbert W. van Hamersvelt, Max A. Viergever +2
Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We propose an algorithm that…
Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information
Steffen Bruns, Jelmer M. Wolterink, Robbert W. van Hamersvelt +3
Accurate segmentation of the left ventricle myocardium in cardiac CT angiography (CCTA) is essential for e.g. the assessment of myocardial perfusion. Automatic deep learning method…
CNN-based Landmark Detection in Cardiac CTA Scans
Julia M. H. Noothout, Bob D. de Vos, Jelmer M. Wolterink +2
Fast and accurate anatomical landmark detection can benefit many medical image analysis methods. Here, we propose a method to automatically detect anatomical landmarks in medical i…
Blood Vessel Geometry Synthesis using Generative Adversarial Networks
Jelmer M. Wolterink, Tim Leiner, Ivana Isgum
Computationally synthesized blood vessels can be used for training and evaluation of medical image analysis applications. We propose a deep generative model to synthesize blood ves…
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography
Majd Zreik, Robbert W. van Hamersvelt, Jelmer M. Wolterink +3
Various types of atherosclerotic plaque and varying grades of stenosis could lead to different management of patients with coronary artery disease. Therefore, it is crucial to dete…
Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
Majd Zreik, Nikolas Lessmann, Robbert W. van Hamersvelt +5
In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during…
Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images
Jelmer M. Wolterink, Tim Leiner, Max A. Viergever +1
Segmentation of the heart in cardiac cine MR is clinically used to quantify cardiac function. We propose a fully automatic method for segmentation and disease classification using…
Automatic Segmentation of the Left Ventricle in Cardiac CT Angiography Using Convolutional Neural Network
Majd Zreik, Tim Leiner, Bob D. de Vos +3
Accurate delineation of the left ventricle (LV) is an important step in evaluation of cardiac function. In this paper, we present an automatic method for segmentation of the LV in…
ConvNet-Based Localization of Anatomical Structures in 3D Medical Images
Bob D. de Vos, Jelmer M. Wolterink, Pim A. de Jong +3
Localization of anatomical structures is a prerequisite for many tasks in medical image analysis. We propose a method for automatic localization of one or more anatomical structure…
Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease
Jelmer M. Wolterink, Tim Leiner, Max A. Viergever +1
We propose an automatic method using dilated convolutional neural networks (CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR (CMR) of patients with cong…
Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities
Pim Moeskops, Jelmer M. Wolterink, Bas H. M. van der Velden +4
Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imag…