| 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, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 314 | 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 | 260 | 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, 2742-2752, 2020 | 53 | 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 | 52 | 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, 1831-1841, 2020 | 50 | 2020 |
| Optimization with soft dice can lead to a volumetric bias J Bertels, D Robben, D Vandermeulen, P Suetens Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2020 | 31 | 2020 |
| 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 69 (7), 2153-2164, 2021 | 27 | 2021 |
| 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 | 27 | 2020 |
| 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 | 25 | 2021 |
| Contra-lateral information CNN for core lesion segmentation based on native CTP in acute stroke J Bertels, D Robben, D Vandermeulen, P Suetens Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019 | 20 | 2019 |
| 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 | 19 | 2021 |
| Medical Image Computing and Computer Assisted Intervention–MICCAI 2019 J Bertels, T Eelbode, M Berman, D Vandermeulen, F Maes, R Bisschops, ... Lect Notes Comput Sc 10, 978-3, 2019 | 12 | 2019 |
| 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 | 11 | 2022 |
| On the relationship between calibrated predictors and unbiased volume estimation T Popordanoska, J Bertels, D Vandermeulen, F Maes, MB Blaschko Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 10 | 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 | 10 | 2020 |
| DeepVoxNet: voxel‐wise prediction for 3D images D Robben, J Bertels, S Willems, D Vandermeulen, F Maes, P Suetens | 9 | 2018 |
| Dice semimetric losses: Optimizing the dice score with soft labels Z Wang, T Popordanoska, J Bertels, R Lemmens, MB Blaschko International Conference on Medical Image Computing and Computer-Assisted …, 2023 | 6 | 2023 |
| 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 | 6 | 2021 |
| USE-evaluator: Performance metrics for medical image segmentation models supervised by uncertain, small or empty reference annotations in neuroimaging S Ostmeier, B Axelrod, F Isensee, J Bertels, M Mlynash, S Christensen, ... Medical Image Analysis 90, 102927, 2023 | 4 | 2023 |
| Convolutional neural networks for medical image segmentation J Bertels, D Robben, R Lemmens, D Vandermeulen arXiv preprint arXiv:2211.09562, 2022 | 4 | 2022 |
Vandermeulen DirkProfessor of medical and forensic image analysis KU Leuven ESAT/PSIVerified email at kuleuven.be
