23 citations · 36 across the 4 of their papers we have counts for
6 papers
Inferring a Third Spatial Dimension from 2D Histological Images
Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof +2
Histological images are obtained by transmitting light through a tissue specimen that has been stained in order to produce contrast. This process results in 2D images of the specim…
Isointense infant brain MRI segmentation with a dilated convolutional neural network
Pim Moeskops, Josien P. W. Pluim
Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplan…
Automatic segmentation of the intracranialvolume in fetal MR images
N. Khalili, P. Moeskops, N. H. P. Claessens +7
MR images of the fetus allow non-invasive analysis of the fetal brain. Quantitative analysis of fetal brain development requires automatic brain tissue segmentation that is typical…
Adversarial training and dilated convolutions for brain MRI segmentation
Pim Moeskops, Mitko Veta, Maxime W. Lafarge +2
Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversa…
Exploring the similarity of medical imaging classification problems
Veronika Cheplygina, Pim Moeskops, Mitko Veta +2
Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem of meta-learning -- predicting which methods will perform well in an unseen class…
Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation
Mitko Veta, Paul J. van Diest, Josien P. W. Pluim
The size of nuclei in histological preparations from excised breast tumors is predictive of patient outcome (large nuclei indicate poor outcome). Pathologists take into account nuc…