1.3k citations · 6.1k across the 35 of their papers we have counts for
35 papers
Effect of latent space distribution on the segmentation of images with multiple annotations
Ishaan Bhat, Josien P. W. Pluim, Max A. Viergever +1
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution t…
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection
Ishaan Bhat, Josien P. W. Pluim, Max A. Viergever +1
Deep learning techniques show success in detecting objects in medical images, but still suffer from false-positive predictions that may hinder accurate diagnosis. The estimated unc…
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…
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
Bas H. M. van der Velden, Hugo J. Kuijf, Kenneth G. A. Gilhuijs +1
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis.…
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…
Liver segmentation and metastases detection in MR images using convolutional neural networks
Mariëlle J. A. Jansen, Hugo J. Kuijf, Maarten Niekel +4
Primary tumors have a high likelihood of developing metastases in the liver and early detection of these metastases is crucial for patient outcome. We propose a method based on con…
Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data
Fenghua Guo, Alexander Leemans, Max A. Viergever +2
Spherical deconvolution is a widely used approach to quantify fiber orientation distribution from diffusion MRI data. The damped Richardson-Lucy (dRL) is developed to perform robus…
Harmonization of diffusion MRI datasets with adaptive dictionary learning
Samuel St-Jean, Max A. Viergever, Alexander Leemans
Diffusion magnetic resonance imaging is a noninvasive imaging technique that can indirectly infer the microstructure of tissues and provide metrics which are subject to normal vari…
SMART tracking: Simultaneous anatomical imaging and real-time passive device tracking for MR-guided interventions
Frank Zijlstra, Max A. Viergever, Peter R. Seevinck
Purpose: This study demonstrates a proof of concept of a method for simultaneous anatomical imaging and real-time (SMART) passive device tracking for MR-guided interventions. Metho…
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…
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…
Automated characterization of noise distributions in diffusion MRI data
Samuel St-Jean, Alberto De Luca, Chantal M. W. Tax +2
Knowledge of the noise distribution in diffusion MRI is the centerpiece to quantify uncertainties arising from the acquisition process. Accurate estimation beyond textbook distribu…
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…
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser +41
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are o…
Reducing variability in along-tract analysis with diffusion profile realignment
Samuel St-Jean, Maxime Chamberland, Max A. Viergever +1
Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction of the brain's white matter structures through tractography. Analyzing dMRI measures along the trajecto…
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…
Direct Prediction of Cardiovascular Mortality from Low-dose Chest CT using Deep Learning
Sanne G. M. van Velzen, Majd Zreik, Nikolas Lessmann +4
Cardiovascular disease (CVD) is a leading cause of death in the lung cancer screening population. Chest CT scans made in lung cancer screening are suitable for identification of pa…
A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
Bob D. de Vos, Floris F. Berendsen, Max A. Viergever +3
Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep lea…
Automatic, fast and robust characterization of noise distributions for diffusion MRI
Samuel St-Jean, Alberto De Luca, Max A. Viergever +1
Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging…
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…
Direct and Real-Time Cardiovascular Risk Prediction
Bob D. de Vos, Nikolas Lessmann, Pim A. de Jong +2
Coronary artery calcium (CAC) burden quantified in low-dose chest CT is a predictor of cardiovascular events. We propose an automatic method for CAC quantification, circumventing i…