333 citations · 1.9k across the 64 of their papers we have counts for
23 papers · 1 filter
SegReg: Latent Space Regularization for Improved Medical Image Segmentation
Puru Vaish, Amin Ranem, Felix Meister +3
Medical image segmentation models are typically optimised with voxel-wise losses that constrain predictions only in the output space. This leaves latent feature representations lar…
Knowledge Distillation for Continual Learning of Biomedical Neural Fields
Wouter Visser, Jelmer M. Wolterink
Neural fields are increasingly used as a light-weight, continuous, and differentiable signal representation in (bio)medical imaging. However, unlike discrete signal representations…
Beyond Pixels: Medical Image Quality Assessment with Implicit Neural Representations
Caner Özer, Patryk Rygiel, Bram de Wilde +2
Artifacts pose a significant challenge in medical imaging, impacting diagnostic accuracy and downstream analysis. While image-based approaches for detecting artifacts can be effect…
Learning Hemodynamic Scalar Fields on Coronary Artery Meshes: A Benchmark of Geometric Deep Learning Models
Guido Nannini, Julian Suk, Patryk Rygiel +7
Coronary artery disease, caused by the narrowing of coronary vessels due to atherosclerosis, is the leading cause of death worldwide. The diagnostic gold standard, fractional flow…
Vestibular schwannoma growth prediction from longitudinal MRI by time conditioned neural fields
Yunjie Chen, Jelmer M. Wolterink, Olaf M. Neve +5
Vestibular schwannomas (VS) are benign tumors that are generally managed by active surveillance with MRI examination. To further assist clinical decision-making and avoid overtreat…
Brain-Shift: Unsupervised Pseudo-Healthy Brain Synthesis for Novel Biomarker Extraction in Chronic Subdural Hematoma
Baris Imre, Elina Thibeau-Sutre, Jorieke Reimer +2
Chronic subdural hematoma (cSDH) is a common neurological condition characterized by the accumulation of blood between the brain and the dura mater. This accumulation of blood can…
Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation
Patryk Rygiel, Dieuwertje Alblas, Christoph Brune +2
Personalized 3D vascular models can aid in a range of diagnostic, prognostic, and treatment-planning tasks relevant to cardiovascular disease management. Deep learning provides a m…
Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Pablo Laso, Stefano Cerri, Annabel Sorby-Adams +21
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated se…
CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation
Yunjie Chen, Marius Staring, Olaf M. Neve +5
Multi-sequence magnetic resonance imaging (MRI) has found wide applications in both modern clinical studies and deep learning research. However, in clinical practice, it frequently…
Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRI
Elina Thibeau-Sutre, Dieuwertje Alblas, Sophie Buurman +2
The application of deep learning models to large-scale data sets requires means for automatic quality assurance. We have previously developed a fully automatic algorithm for caroti…
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
Philippe Weitz, Masi Valkonen, Leslie Solorzano +38
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability…
Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progression
Dieuwertje Alblas, Marieke Hofman, Christoph Brune +2
Abdominal aortic aneurysms (AAAs) are progressive dilatations of the abdominal aorta that, if left untreated, can rupture with lethal consequences. Imaging-based patient monitoring…
Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling
Dieuwertje Alblas, Christoph Brune, Kak Khee Yeung +1
Personalised 3D vascular models are valuable for diagnosis, prognosis and treatment planning in patients with cardiovascular disease. Traditionally, such models have been construct…
Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors
Dieuwertje Alblas, Christoph Brune, Jelmer M. Wolterink
Carotid artery vessel wall thickness measurement is an essential step in the monitoring of patients with atherosclerosis. This requires accurate segmentation of the vessel wall, i.…
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…
Automatic Online Quality Control of Synthetic CTs
Louis D. van Harten, Jelmer M. Wolterink, Joost J. C. Verhoeff +1
Accurate MR-to-CT synthesis is a requirement for MR-only workflows in radiotherapy (RT) treatment planning. In recent years, deep learning-based approaches have shown impressive re…
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…
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…
Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy
Mohamed S. Elmahdy, Jelmer M. Wolterink, Hessam Sokooti +2
Joint image registration and segmentation has long been an active area of research in medical imaging. Here, we reformulate this problem in a deep learning setting using adversaria…
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…