788 citations · 1.2k across the 6 of their papers we have counts for
7 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…
Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities
Pim Moeskops, Jelmer M. Wolterink, Bas H. M. van der Velden +4
Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imag…