| The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping A Zwanenburg, M Vallières, MA Abdalah, HJWL Aerts, V Andrearczyk, ... Radiology 295 (2), 328-338, 2020 | 2216 | 2020 |
| nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation F Isensee, PF Jaeger, SAA Kohl, J Petersen, KH Maier-Hein Nature methods 18 (2), 203-211, 2021 | 1985 | 2021 |
| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1528 | 2018 |
| Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? O Bernard, A Lalande, C Zotti, F Cervenansky, X Yang, PA Heng, I Cetin, ... IEEE transactions on medical imaging 37 (11), 2514-2525, 2018 | 1158 | 2018 |
| The challenge of mapping the human connectome based on diffusion tractography KH Maier-Hein, PF Neher, JC Houde, MA Côté, E Garyfallidis, J Zhong, ... Nature communications 8 (1), 1349, 2017 | 1031 | 2017 |
| The future of digital health with federated learning N Rieke, J Hancox, W Li, F Milletari, HR Roth, S Albarqouni, S Bakas, ... NPJ digital medicine 3 (1), 119, 2020 | 1028 | 2020 |
| The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680, 2023 | 724 | 2023 |
| 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 | 719 | 2018 |
| Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge F Isensee, P Kickingereder, W Wick, M Bendszus, KH Maier-Hein Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018 | 571 | 2018 |
| Deep MRI brain extraction: A 3D convolutional neural network for skull stripping J Kleesiek, G Urban, A Hubert, D Schwarz, K Maier-Hein, M Bendszus, ... NeuroImage 129, 460-469, 2016 | 527 | 2016 |
| A probabilistic u-net for segmentation of ambiguous images S Kohl, B Romera-Paredes, C Meyer, J De Fauw, JR Ledsam, ... Advances in neural information processing systems 31, 2018 | 472 | 2018 |
| No new-net F Isensee, P Kickingereder, W Wick, M Bendszus, KH Maier-Hein Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019 | 471 | 2019 |
| Methodological considerations on tract-based spatial statistics (TBSS) M Bach, FB Laun, A Leemans, CMW Tax, GJ Biessels, B Stieltjes, ... Neuroimage 100, 358-369, 2014 | 471 | 2014 |
| 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 | 446 | 2017 |
| The Medical Imaging Interaction Toolkit: challenges and advances: 10 years of open-source development M Nolden, S Zelzer, A Seitel, D Wald, M Müller, AM Franz, D Maleike, ... International journal of computer assisted radiology and surgery 8, 607-620, 2013 | 432 | 2013 |
| The medical segmentation decathlon M Antonelli, A Reinke, S Bakas, K Farahani, A Kopp-Schneider, ... Nature communications 13 (1), 4128, 2022 | 389 | 2022 |
| TractSeg-Fast and accurate white matter tract segmentation J Wasserthal, P Neher, KH Maier-Hein NeuroImage 183, 239-253, 2018 | 388 | 2018 |
| Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models P Kickingereder, S Burth, A Wick, M Götz, O Eidel, HP Schlemmer, ... Radiology 280 (3), 880-889, 2016 | 372 | 2016 |
| The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge N Heller, F Isensee, KH Maier-Hein, X Hou, C Xie, F Li, Y Nan, G Mu, ... Medical image analysis 67, 101821, 2021 | 348 | 2021 |
| CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation AE Kavur, NS Gezer, M Barış, S Aslan, PH Conze, V Groza, DD Pham, ... Medical Image Analysis 69, 101950, 2021 | 328 | 2021 |
Fabian IsenseeHIP Applied Computer Vision Lab, Division of Medical Image Computing, German Cancer Research CenterVerified email at dkfz-heidelberg.de
