Pim Moeskops
Pim Moeskops
Verified email at quantib.com
TitleCited byYear
Automatic segmentation of MR brain images with a convolutional neural network
P Moeskops, MA Viergever, AM Mendrik, LS de Vries, M Benders, I Isgum
IEEE Transactions on Medical Imaging 35 (5), 1252-1261, 2016
Deep learning for multi-task medical image segmentation in multiple modalities
P Moeskops, JM Wolterink, BHM van der Velden, KGA Gilhuijs, T Leiner, ...
International Conference on Medical Image Computing and Computer-Assisted†…, 2016
Structural and resting state functional connectivity of the subthalamic nucleus: identification of motor STN parts and the hyperdirect pathway
EJL Brunenberg, P Moeskops, WH Backes, C Pollo, L Cammoun, ...
PloS one 7 (6), e39061, 2012
Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge
I Išgum, MJNL Benders, B Avants, MJ Cardoso, SJ Counsell, EF Gomez, ...
Medical image analysis 20 (1), 135-151, 2015
Adversarial training and dilated convolutions for brain MRI segmentation
P Moeskops, M Veta, MW Lafarge, KAJ Eppenhof, JPW Pluim
Deep learning in medical image analysis and multimodal learning for clinical†…, 2017
Automatic segmentation of MR brain images of preterm infants using supervised classification
P Moeskops, MJNL Benders, SM Chiţǎ, KJ Kersbergen, F Groenendaal, ...
NeuroImage 118, 628-641, 2015
Development of cortical morphology evaluated with longitudinal MR brain images of preterm infants
P Moeskops, MJNL Benders, KJ Kersbergen, F Groenendaal, LS de Vries, ...
PLOS ONE 10 (7), e0131552, 2015
Domain-adversarial neural networks to address the appearance variability of histopathology images
MW Lafarge, JPW Pluim, KAJ Eppenhof, P Moeskops, M Veta
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical†…, 2017
Relation between clinical risk factors, early cortical changes, and neurodevelopmental outcome in preterm infants
KJ Kersbergen, F Leroy, I Išgum, F Groenendaal, LS de Vries, ...
Neuroimage 142, 301-310, 2016
Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI
P Moeskops, J de Bresser, HJ Kuijf, AM Mendrik, GJ Biessels, JPW Pluim, ...
NeuroImage: Clinical 17, 251-262, 2018
Perioperative neonatal brain injury is associated with worse school‐age neurodevelopment in children with critical congenital heart disease
NHP Claessens, SO Algra, TL Ouwehand, NJG Jansen, R Schappin, ...
Developmental Medicine & Child Neurology 60 (10), 1052-1058, 2018
Deformable image registration using convolutional neural networks
KAJ Eppenhof, MW Lafarge, P Moeskops, M Veta, JPW Pluim
Medical Imaging 2018: Image Processing 10574, 105740S, 2018
Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images
P Moeskops, MA Viergever, MJNL Benders, I Išgum
Medical Imaging 2015: Image Processing 9413, 941315, 2015
Benchmark on automatic 6-month-old infant brain segmentation algorithms: the iSeg-2017 challenge
L Wang, D Nie, G Li, … Puybareau, J Dolz, Q Zhang, F Wang, J Xia, Z Wu, ...
IEEE transactions on medical imaging, 2019
Delayed cortical gray matter development in neonates with severe congenital heart disease
NHP Claessens, P Moeskops, A Buchmann, B Latal, W Knirsch, I Scheer, ...
Pediatric Research 80, 668–674, 2016
Automatic segmentation of the preterm neonatal brain with MRI using supervised classification
SM Chiţă, M Benders, P Moeskops, KJ Kersbergen, MA Viergever, I Išgum
Medical Imaging 2013: Image Processing 8669, 86693X, 2013
Severe retinopathy of prematurity is associated with reduced cerebellar and brainstem volumes at term and neurodevelopmental deficits at 2 years
FJ Drost, K Keunen, P Moeskops, NHP Claessens, F Van Kalken, I Išgum, ...
Pediatric research 83 (4), 818, 2018
Assessment of quantitative cortical biomarkers in the developing brain of preterm infants
P Moeskops, MJNL Benders, PC Pearlman, KJ Kersbergen, A Leemans, ...
Medical Imaging 2013: Computer-Aided Diagnosis 8670, 867011, 2013
Automatic segmentation of the intracranial volume in fetal MR images
N Khalili, P Moeskops, NHP Claessens, S Scherpenzeel, E Turk, ...
Fetal, Infant and Ophthalmic Medical Image Analysis, 42-51, 2017
Isointense infant brain MRI segmentation with a dilated convolutional neural network
P Moeskops, JPW Pluim
arXiv preprint arXiv:1708.02757, 2017
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