333 citations · 1.9k across the 64 of their papers we have counts for
65 papers
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
When Does Equivariance Help? Canonical Alignment in Neural Fluid Surrogates
Patryk Rygiel, Julian Suk, Kak Khee Yeung +2
Neural surrogates can accelerate computational fluid dynamics (CFD) simulations by orders of magnitude, but practical deployment in engineering and healthcare applications requires…
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…
A deep learning model to reduce agent dose for contrast-enhanced MRI of the cerebellopontine angle cistern
Yunjie Chen, Rianne A. Weber, Olaf M. Neve +6
Objectives: To evaluate a deep learning (DL) model for reducing the agent dose of contrast-enhanced T1-weighted MRI (T1ce) of the cerebellopontine angle (CPA) cistern. Materials an…
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…
Consistent View Alignment Improves Foundation Models for 3D Medical Image Segmentation
Puru Vaish, Felix Meister, Tobias Heimann +2
Many recent approaches in representation learning implicitly assume that uncorrelated views of a data point are sufficient to learn meaningful representations for various downstrea…
GReAT: leveraging geometric artery data to improve wall shear stress assessment
Julian Suk, Jolanda J. Wentzel, Patryk Rygiel +3
Leveraging big data for patient care is promising in many medical fields such as cardiovascular health. For example, hemodynamic biomarkers like wall shear stress could be assessed…
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…
Wall Shear Stress Estimation in Abdominal Aortic Aneurysms: Towards Generalisable Neural Surrogate Models
Patryk Rygiel, Julian Suk, Christoph Brune +2
Abdominal aortic aneurysms (AAAs) are pathologic dilatations of the abdominal aorta posing a high fatality risk upon rupture. Studying AAA progression and rupture risk often involv…
Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces
Dieuwertje Alblas, Patryk Rygiel, Julian Suk +5
Abdominal aortic aneurysms (AAAs) are progressive focal dilatations of the abdominal aorta. AAAs may rupture, with a survival rate of only 20\%. Current clinical guidelines recomme…
Data-Agnostic Augmentations for Unknown Variations: Out-of-Distribution Generalisation in MRI Segmentation
Puru Vaish, Felix Meister, Tobias Heimann +2
Medical image segmentation models are often trained on curated datasets, leading to performance degradation when deployed in real-world clinical settings due to mismatches between…
Steerable Anatomical Shape Synthesis with Implicit Neural Representations
Bram de Wilde, Max T. Rietberg, Guillaume Lajoinie +1
Generative modeling of anatomical structures plays a crucial role in virtual imaging trials, which allow researchers to perform studies without the costs and constraints inherent t…
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation
Patryk Rygiel, Julian Suk, Kak Khee Yeung +2
Hemodynamic parameters such as pressure and wall shear stress play an important role in diagnosis, prognosis, and treatment planning in cardiovascular diseases. These parameters ca…
Waveform-Specific Performance of Deep Learning-Based Super-Resolution for Ultrasound Contrast Imaging
Rienk Zorgdrager, Nathan Blanken, Jelmer M. Wolterink +2
Resolving arterial flows is essential for understanding cardiovascular pathologies, improving diagnosis, and monitoring patient condition. Ultrasound contrast imaging uses microbub…
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…
Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior
Julian Suk, Guido Nannini, Patryk Rygiel +4
Cardiovascular hemodynamic fields provide valuable medical decision markers for coronary artery disease. Computational fluid dynamics (CFD) is the gold standard for accurate, non-i…
Physics-informed graph neural networks for flow field estimation in carotid arteries
Julian Suk, Dieuwertje Alblas, Barbara A. Hutten +4
Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology such as atherosclerosis. Non-invasive, in-vivo measurement of these quantities can only be…
Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging
Simone Garzia, Patryk Rygiel, Sven Dummer +3
Time-resolved three-dimensional flow MRI (4D flow MRI) provides a unique non-invasive solution to visualize and quantify hemodynamics in blood vessels such as the aortic arch. Howe…
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…
Neural Fields for 3D Tracking of Anatomy and Surgical Instruments in Monocular Laparoscopic Video Clips
Beerend G. A. Gerats, Jelmer M. Wolterink, Seb P. Mol +1
Laparoscopic video tracking primarily focuses on two target types: surgical instruments and anatomy. The former could be used for skill assessment, while the latter is necessary fo…
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…
LaB-GATr: geometric algebra transformers for large biomedical surface and volume meshes
Julian Suk, Baris Imre, Jelmer M. Wolterink
Many anatomical structures can be described by surface or volume meshes. Machine learning is a promising tool to extract information from these 3D models. However, high-fidelity me…
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…