| ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI O Maier, BH Menze, J Von der Gablentz, L Häni, MP Heinrich, M Liebrand, ... Medical image analysis 35, 250-269, 2017 | 502 | 2017 |
| ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI S Winzeck, A Hakim, R McKinley, JA Pinto, V Alves, C Silva, M Pisov, ... Frontiers in neurology 9, 679, 2018 | 167 | 2018 |
| Benefits of deep learning for delineation of organs at risk in head and neck cancer J Van der Veen, S Willems, S Deschuymer, D Robben, W Crijns, F Maes, ... Radiotherapy and Oncology 138, 68-74, 2019 | 110 | 2019 |
| Simultaneous segmentation and anatomical labeling of the cerebral vasculature D Robben, E Türetken, S Sunaert, V Thijs, G Wilms, P Fua, F Maes, ... Medical image analysis 32, 201-215, 2016 | 82 | 2016 |
| Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning D Robben, AMM Boers, HA Marquering, LLCM Langezaal, YB Roos, ... Medical image analysis 59, 101589, 2020 | 79 | 2020 |
| ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset MR Hernandez Petzsche, E de la Rosa, U Hanning, R Wiest, ... Scientific data 9 (1), 762, 2022 | 69 | 2022 |
| Variations in the Circle of Willis in a large population sample using 3D TOF angiography: The Tromsø Study LB Hindenes, AK Håberg, LH Johnsen, EB Mathiesen, D Robben, ... PLoS One 15 (11), e0241373, 2020 | 60 | 2020 |
| Predicting infarct core from computed tomography perfusion in acute ischemia with machine learning: Lessons from the ISLES challenge A Hakim, S Christensen, S Winzeck, MG Lansberg, MW Parsons, C Lucas, ... Stroke 52 (7), 2328-2337, 2021 | 57 | 2021 |
| 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 | 55 | 2020 |
| Detection of vertebral fractures in CT using 3D convolutional neural networks J Nicolaes, S Raeymaeckers, D Robben, G Wilms, D Vandermeulen, ... Computational Methods and Clinical Applications for Spine Imaging: 6th …, 2020 | 35 | 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 |
| 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 |
| Prediction of stroke infarct growth rates by baseline perfusion imaging A Wouters, D Robben, S Christensen, HA Marquering, YB Roos, ... Stroke 53 (2), 569-577, 2022 | 25 | 2022 |
| A Voxel-wise, cascaded classification approach to ischemic stroke lesion segmentation D Robben, D Christiaens, JR Rangarajan, J Gelderblom, P Joris, F Maes, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2016 | 23 | 2016 |
| A reliable and time‐saving semiautomatic spike‐template–based analysis of interictal EEG–f MRI S Tousseyn, P Dupont, D Robben, K Goffin, S Sunaert, W Van Paesschen Epilepsia 55 (12), 2048-2058, 2014 | 22 | 2014 |
| 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 | 21 | 2019 |
| Clinical implementation of DeepVoxNet for auto-delineation of organs at risk in head and neck cancer patients in radiotherapy S Willems, W Crijns, A La Greca Saint-Esteven, J Van Der Veen, ... OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy …, 2018 | 21 | 2018 |
| Unsupervised 3d brain anomaly detection J Simarro Viana, E de la Rosa, T Vande Vyvere, D Robben, DM Sima, ... International MICCAI Brainlesion Workshop, 133-142, 2020 | 20 | 2020 |
| Anatomical labeling of the Circle of Willis using maximum a posteriori graph matching D Robben, S Sunaert, V Thijs, G Wilms, F Maes, P Suetens Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th …, 2013 | 19 | 2013 |
| The role of medical image computing and machine learning in healthcare F Maes, D Robben, D Vandermeulen, P Suetens Artificial intelligence in medical imaging: opportunities, applications and …, 2019 | 18 | 2019 |
Paul Suetensprof. em. KU Leuven ESAT/PSI在 esat.kuleuven.be 的电子邮件经过验证
