Jeroen Bertels
Jeroen Bertels
PhD student
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Optimizing the dice score and jaccard index for medical image segmentation: Theory and practice
J Bertels, T Eelbode, M Berman, D Vandermeulen, F Maes, R Bisschops, ...
International conference on medical image computing and computer-assisted …, 2019
Optimization for medical image segmentation: theory and practice when evaluating with dice score or Jaccard index
T Eelbode, J Bertels, M Berman, D Vandermeulen, F Maes, R Bisschops, ...
IEEE Transactions on Medical Imaging 39 (11), 3679-3690, 2020
Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT
X Tang, E Jafargholi Rangraz, W Coudyzer, J Bertels, D Robben, ...
European journal of nuclear medicine and molecular imaging 47 (12), 2742-2752, 2020
Effect of lower third molar segmentations on automated tooth development staging using a convolutional neural network
R Merdietio Boedi, N Banar, J De Tobel, J Bertels, D Vandermeulen, ...
Journal of Forensic Sciences 65 (2), 481-486, 2020
Towards fully automated third molar development staging in panoramic radiographs
N Banar, J Bertels, F Laurent, RM Boedi, J De Tobel, P Thevissen, ...
International Journal of Legal Medicine 134 (5), 1831-1841, 2020
Optimization with soft dice can lead to a volumetric bias
J Bertels, D Robben, D Vandermeulen, P Suetens
International MICCAI Brainlesion Workshop, 89-97, 2019
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
S Tilborghs, I Dirks, L Fidon, S Willems, T Eelbode, J Bertels, B Ilsen, ...
arXiv preprint arXiv:2007.15546, 2020
Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty
J Bertels, D Robben, D Vandermeulen, P Suetens
Medical Image Analysis 67, 101833, 2021
Contra-lateral information CNN for core lesion segmentation based on native CTP in acute stroke
J Bertels, D Robben, D Vandermeulen, P Suetens
International MICCAI Brainlesion Workshop, 263-270, 2018
Post training uncertainty calibration of deep networks for medical image segmentation
AJ Rousseau, T Becker, J Bertels, MB Blaschko, D Valkenborg
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 1052-1056, 2021
Explainable-by-design semi-supervised representation learning for covid-19 diagnosis from ct imaging
AD Berenguer, H Sahli, B Joukovsky, M Kvasnytsia, I Dirks, ...
arXiv preprint arXiv:2011.11719, 2020
DeepVoxNet: voxel‐wise prediction for 3D images
D Robben, J Bertels, S Willems, D Vandermeulen, F Maes, P Suetens
On the relationship between calibrated predictors and unbiased volume estimation
T Popordanoska, J Bertels, D Vandermeulen, F Maes, MB Blaschko
International Conference on Medical Image Computing and Computer-Assisted …, 2021
Cross-modal distillation to improve MRI-based brain tumor segmentation with missing MRI sequences
M Rahimpour, J Bertels, A Radwan, H Vandermeulen, S Sunaert, ...
IEEE Transactions on Biomedical Engineering, 2021
Improving T1w MRI-based brain tumor segmentation using cross-modal distillation
M Rahimpour, J Bertels, D Vandermeulen, F Maes, K Goffin, M Koole
Medical Imaging 2021: Image Processing 11596, 261-268, 2021
Automated breast cancer risk estimation on routine CT thorax scans by deep learning segmentation
S De Buck, J Bertels, C Vanbilsen, T Dewaele, C Van Ongeval, ...
Medical Imaging 2020: Computer-Aided Diagnosis 11314, 494-499, 2020
Deep learning-based dental implant recognition using synthetic X-ray images
A Kohlakala, J Coetzer, J Bertels, D Vandermeulen
Medical & Biological Engineering & Computing 60 (10), 2951-2968, 2022
Evaluation of Medical Image Segmentation Models for Uncertain, Small or Empty Reference Annotations
S Ostmeier, B Axelrod, J Bertels, F Isensee, MG Lansberg, S Christensen, ...
arXiv preprint arXiv:2209.13008, 2022
The Dice loss in the context of missing or empty labels: Introducing and
S Tilborghs, J Bertels, D Robben, D Vandermeulen, F Maes
arXiv preprint arXiv:2207.09521, 2022
Improving final infarct prediction in acute ischemic stroke using convolutional neural networks
J Bertels
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