| nnu-net: Self-adapting framework for u-net-based medical image segmentation F Isensee, J Petersen, A Klein, D Zimmerer, PF Jaeger, S Kohl, ... arXiv preprint arXiv:1809.10486, 2018 | 1095 | 2018 |
| TotalSegmentator: robust segmentation of 104 anatomic structures in CT images J Wasserthal, HC Breit, MT Meyer, M Pradella, D Hinck, AW Sauter, ... Radiology: Artificial Intelligence 5 (5), e230024, 2023 | 820 | 2023 |
| TractSeg-Fast and accurate white matter tract segmentation J Wasserthal, P Neher, KH Maier-Hein NeuroImage 183, 239-253, 2018 | 590 | 2018 |
| Tract orientation mapping for bundle-specific tractography J Wasserthal, PF Neher, KH Maier-Hein International conference on medical image computing and computer-assisted …, 2018 | 185 | 2018 |
| Multiparametric mapping of white matter microstructure in catatonia J Wasserthal, KH Maier-Hein, PF Neher, G Northoff, KM Kubera, S Fritze, ... Neuropsychopharmacology 45 (10), 1750-1757, 2020 | 75 | 2020 |
| Tractography reproducibility challenge with empirical data (TraCED): the 2017 ISMRM diffusion study group challenge V Nath, KG Schilling, P Parvathaneni, Y Huo, JA Blaber, AE Hainline, ... Journal of Magnetic Resonance Imaging 51 (1), 234-249, 2020 | 51 | 2020 |
| batchgenerators—a python framework for data augmentation F Isensee, P Jäger, J Wasserthal, D Zimmerer, J Petersen, S Kohl, ... Zenodo 3632567, 2020 | 49 | 2020 |
| nnU-Net: Self-adapting framework for u-net-based medical image segmentation (2018) F Isensee, J Petersen, A Klein, D Zimmerer, PF Jaeger, S Kohl, ... arXiv preprint arXiv:1809.10486, 1809 | 33 | 1809 |
| Medshapenet–a large-scale dataset of 3d medical shapes for computer vision J Li, Z Zhou, J Yang, A Pepe, C Gsaxner, G Luijten, C Qu, T Zhang, ... Biomedical Engineering/Biomedizinische Technik 70 (1), 71-90, 2025 | 28 | 2025 |
| Potential of stroke imaging using a new prototype of low-field MRI: a prospective direct 0.55 T/1.5 T scanner comparison T Rusche, HC Breit, M Bach, J Wasserthal, J Gehweiler, S Manneck, ... Journal of clinical medicine 11 (10), 2798, 2022 | 26 | 2022 |
| Totalsegmentator mri: Sequence-independent segmentation of 59 anatomical structures in mr images TA D'Antonoli, LK Berger, AK Indrakanti, N Vishwanathan, J Weiß, M Jung, ... arXiv preprint arXiv:2405.19492, 2024 | 24 | 2024 |
| Automated detection of pancreatic cystic lesions on CT using deep learning L Abel, J Wasserthal, T Weikert, AW Sauter, I Nesic, M Obradovic, S Yang, ... Diagnostics 11 (5), 901, 2021 | 20 | 2021 |
| batchgenerators-a python framework for data augmentation (2020) F Isensee, P Jäger, J Wasserthal, D Zimmerer, J Petersen, S Kohl, ... URL https://doi. org/10.5281/zenodo 3632567, 2020 | 14 | 2020 |
| Deep Anatomical Federated Network (Dafne): an open client/server framework for the continuous collaborative improvement of deep-learning-based medical image segmentation F Santini, J Wasserthal, A Agosti, X Deligianni, KR Keene, HE Kan, ... arXiv preprint arXiv:2302.06352, 2023 | 13 | 2023 |
| White matter microstructure alterations in cortico-striatal networks are associated with parkinsonism in schizophrenia spectrum disorders J Wasserthal, KH Maier-Hein, PF Neher, RC Wolf, G Northoff, ... European Neuropsychopharmacology 50, 64-74, 2021 | 13 | 2021 |
| Pulmonary transit time of cardiovascular magnetic resonance perfusion scans for quantification of cardiopulmonary haemodynamics M Segeroth, DJ Winkel, I Strebel, S Yang, JG van der Stouwe, ... European Heart Journal-Cardiovascular Imaging 24 (8), 1062-1071, 2023 | 11 | 2023 |
| Differentiating axonal loss and demyelination in chronic MS lesions: a novel approach using single streamline diffusivity analysis S Klistorner, MH Barnett, J Wasserthal, C Yiannikas, J Barton, J Parratt, ... PLoS One 16 (1), e0244766, 2021 | 10 | 2021 |
| Prospective assessment of cerebral microbleeds with low-field magnetic resonance imaging (0.55 Tesla MRI) T Rusche, HC Breit, M Bach, J Wasserthal, J Gehweiler, S Manneck, ... Journal of Clinical Medicine 12 (3), 1179, 2023 | 8 | 2023 |
| Direct white matter bundle segmentation using stacked u-nets J Wasserthal, PF Neher, F Isensee, KH Maier-Hein arXiv preprint arXiv:1703.02036, 2017 | 8 | 2017 |
| Machine learning for onset prediction of patients with intracerebral hemorrhage T Rusche, J Wasserthal, HC Breit, U Fischer, R Guzman, J Fiehler, ... Journal of Clinical Medicine 12 (7), 2631, 2023 | 5 | 2023 |
