843 citations · 4.4k across the 41 of their papers we have counts for
12 papers · 1 filter
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…
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 calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
Nikolas Lessmann, Bram van Ginneken, Majd Zreik +4
Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable qua…
Deep MR to CT Synthesis using Unpaired Data
Jelmer M. Wolterink, Anna M. Dinkla, Mark H. F. Savenije +3
MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis. Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned MR and CT training ima…
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 intracranialvolume in fetal MR images
N. Khalili, P. Moeskops, N. H. P. Claessens +7
MR images of the fetus allow non-invasive analysis of the fetal brain. Quantitative analysis of fetal brain development requires automatic brain tissue segmentation that is typical…
End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network
Bob D. de Vos, Floris F. Berendsen, Max A. Viergever +2
In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial tra…
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…
Automatic segmentation of MR brain images with a convolutional neural network
Pim Moeskops, Max A. Viergever, Adriënne M. Mendrik +3
Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the autom…