843 citations · 4.4k across the 41 of their papers we have counts for
12 papers · 1 filter
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing
Bas H. M. van der Velden, Bob D. de Vos, Claudette E. Loo +3
Response of breast cancer to neoadjuvant chemotherapy (NAC) can be monitored using the change in visible tumor on magnetic resonance imaging (MRI). In our current workflow, seed po…
Generative adversarial networks and adversarial methods in biomedical image analysis
Jelmer M. Wolterink, Konstantinos Kamnitsas, Christian Ledig +1
Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game.…
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…
Automatic Segmentation of Thoracic Aorta Segments in Low-Dose Chest CT
Julia M. H. Noothout, Bob D. de Vos, Jelmer M. Wolterink +1
Morphological analysis and identification of pathologies in the aorta are important for cardiovascular diagnosis and risk assessment in patients. Manual annotation is time-consumin…
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…
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…
Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI
Jörg Sander, Bob D. de Vos, Jelmer M. Wolterink +1
Current state-of-the-art deep learning segmentation methods have not yet made a broad entrance into the clinical setting in spite of high demand for such automatic methods. One imp…
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…
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…
Iterative fully convolutional neural networks for automatic vertebra segmentation and identification
Nikolas Lessmann, Bram van Ginneken, Pim A. de Jong +1
Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or…
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…