| 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 | 4936* | 2021 |
| 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 | 2842 | 2020 |
| 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 | 2053 | 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 | 1795 | 2018 |
| 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 | 1250 | 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 | 1010 | 2018 |
| The medical segmentation decathlon M Antonelli, A Reinke, S Bakas, K Farahani, A Kopp-Schneider, ... Nature communications 13 (1), 4128, 2022 | 981 | 2022 |
| 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 | 688 | 2018 |
| 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 | 597 | 2021 |
| Automated brain extraction of multisequence MRI using artificial neural networks F Isensee, M Schell, I Pflueger, G Brugnara, D Bonekamp, U Neuberger, ... Human brain mapping 40 (17), 4952-4964, 2019 | 555 | 2019 |
| No new-net F Isensee, P Kickingereder, W Wick, M Bendszus, KH Maier-Hein Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019 | 551 | 2019 |
| 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 | 524 | 2021 |
| Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study P Kickingereder, F Isensee, I Tursunova, J Petersen, U Neuberger, ... The Lancet Oncology 20 (5), 728-740, 2019 | 402 | 2019 |
| nnU-Net for brain tumor segmentation F Isensee, PF Jäger, PM Full, P Vollmuth, KH Maier-Hein Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2021 | 372 | 2021 |
| Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features F Isensee, PF Jaeger, PM Full, I Wolf, S Engelhardt, KH Maier-Hein Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS …, 2018 | 363 | 2018 |
| Metrics reloaded: recommendations for image analysis validation L Maier-Hein, A Reinke, P Godau, MD Tizabi, F Buettner, E Christodoulou, ... Nature methods 21 (2), 195-212, 2024 | 250* | 2024 |
| Retina U-Net: Embarrassingly simple exploitation of segmentation supervision for medical object detection PF Jaeger, SAA Kohl, S Bickelhaupt, F Isensee, TA Kuder, HP Schlemmer, ... Machine Learning for Health Workshop, 171-183, 2020 | 250 | 2020 |
| International MICCAI brainlesion workshop F Isensee, P Kickingereder, W Wick, M Bendszus, KH Maier-Hein Cham: Springer, 287-297, 2017 | 196 | 2017 |
| Common limitations of image processing metrics: A picture story A Reinke, MD Tizabi, CH Sudre, M Eisenmann, T Rädsch, M Baumgartner, ... arXiv preprint arXiv:2104.05642, 2021 | 186 | 2021 |
| Unsupervised anomaly localization using variational auto-encoders D Zimmerer, F Isensee, J Petersen, S Kohl, K Maier-Hein Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 171 | 2019 |
