18 citations · 32 across the 6 of their papers we have counts for
5 papers · 1 filter
DeepVoxNet2: Yet another CNN framework
Jeroen Bertels, David Robben, Robin Lemmens +1
We know that both the CNN mapping function and the sampling scheme are of paramount importance for CNN-based image analysis. It is clear that both functions operate in the same spa…
Convolutional neural networks for medical image segmentation
Jeroen Bertels, David Robben, Robin Lemmens +1
In this article, we look into some essential aspects of convolutional neural networks (CNNs) with the focus on medical image segmentation. First, we discuss the CNN architecture, t…
Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty
Jeroen Bertels, David Robben, Dirk Vandermeulen +1
The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the simi…
Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
David Robben, Anna M. M. Boers, Henk A. Marquering +9
CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the p…
Perfusion parameter estimation using neural networks and data augmentation
David Robben, Paul Suetens
Perfusion imaging plays a crucial role in acute stroke diagnosis and treatment decision making. Current perfusion analysis relies on deconvolution of the measured signals, an opera…