333 citations · 1.9k across the 64 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…