429 citations · 829 across the 15 of their papers we have counts for
6 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…
Final infarct prediction in acute ischemic stroke
Jeroen Bertels, David Robben, Dirk Vandermeulen +1
This article focuses on the control center of each human body: the brain. We will point out the pivotal role of the cerebral vasculature and how its complex mechanisms may vary bet…
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…
USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotations
Sophie Ostmeier, Brian Axelrod, Jeroen Bertels +6
Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metric…
The Dice loss in the context of missing or empty labels: Introducing and
Sofie Tilborghs, Jeroen Bertels, David Robben +2
Albeit the Dice loss is one of the dominant loss functions in medical image segmentation, most research omits a closer look at its derivative, i.e. the real motor of the optimizati…