429 citations · 829 across the 15 of their papers we have counts for
6 papers · 1 filter
Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels
Zifu Wang, Teodora Popordanoska, Jeroen Bertels +2
The soft Dice loss (SDL) has taken a pivotal role in numerous automated segmentation pipelines in the medical imaging community. Over the last years, some reasons behind its superi…
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…
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…
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice
Jeroen Bertels, Tom Eelbode, Maxim Berman +4
The Dice score and Jaccard index are commonly used metrics for the evaluation of segmentation tasks in medical imaging. Convolutional neural networks trained for image segmentation…