333 citations · 1.9k across the 64 of their papers we have counts for
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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…
SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets
Julian Suk, Christoph Brune, Jelmer M. Wolterink
Hemodynamic velocity fields in coronary arteries could be the basis of valuable biomarkers for diagnosis, prognosis and treatment planning in cardiovascular disease. Velocity field…
Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall
Julian Suk, Pim de Haan, Phillip Lippe +2
Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption i…
Mesh convolutional neural networks for wall shear stress estimation in 3D artery models
Julian Suk, Pim de Haan, Phillip Lippe +2
Computational fluid dynamics (CFD) is a valuable tool for personalised, non-invasive evaluation of hemodynamics in arteries, but its complexity and time-consuming nature prohibit l…
Exploiting Clinically Available Delineations for CNN-based Segmentation in Radiotherapy Treatment Planning
Louis D. van Harten, Jelmer M. Wolterink, Joost J. C. Verhoeff +1
Convolutional neural networks (CNNs) have been widely and successfully used for medical image segmentation. However, CNNs are typically considered to require large numbers of dedic…