| 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 | 803 | 2023 |
| Knowledge-aided convolutional neural network for small organ segmentation Y Zhao, H Li, S Wan, A Sekuboyina, X Hu, G Tetteh, M Piraud, B Menze IEEE journal of biomedical and health informatics 23 (4), 1363-1373, 2019 | 186 | 2019 |
| VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images A Sekuboyina, A Bayat, ME Husseini, M Löffler, H Li, G Tetteh, J Kukačka, ... Medical Image Analysis 73, 102166, 2021 | 175* | 2021 |
| clDice-a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation S Shit, JC Paetzold, A Sekuboyina, I Ezhov, A Unger, A Zhylka, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 144 | 2021 |
| A vertebral segmentation dataset with fracture grading MT Löffler, A Sekuboyina, A Jacob, AL Grau, A Scharr, M El Husseini, ... Radiology: Artificial Intelligence 2 (4), e190138, 2020 | 112 | 2020 |
| DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis H Li, JC Paetzold, A Sekuboyina, F Kofler, J Zhang, JS Kirschke, ... arXiv preprint arXiv:1904.12894, 2019 | 80 | 2019 |
| Btrfly net: Vertebrae labelling with energy-based adversarial learning of local spine prior A Sekuboyina, M Rempfler, J Kukačka, G Tetteh, A Valentinitsch, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 70 | 2018 |
| A localisation-segmentation approach for multi-label annotation of lumbar vertebrae using deep nets A Sekuboyina, A Valentinitsch, JS Kirschke, BH Menze arXiv preprint arXiv:1703.04347, 2017 | 64 | 2017 |
| Automatic opportunistic osteoporosis screening in routine CT: improved prediction of patients with prevalent vertebral fractures compared to DXA MT Löffler, A Jacob, A Scharr, N Sollmann, E Burian, M El Husseini, ... European radiology 31, 6069-6077, 2021 | 58 | 2021 |
| A convolutional neural network approach for abnormality detection in Wireless Capsule Endoscopy AK Sekuboyina, ST Devarakonda, CS Seelamantula 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017 | 57 | 2017 |
| Attention-driven deep learning for pathological spine segmentation A Sekuboyina, J Kukačka, JS Kirschke, BH Menze, A Valentinitsch International workshop on computational methods and clinical applications in …, 2017 | 55 | 2017 |
| Labeling vertebrae with two-dimensional reformations of multidetector CT images: an adversarial approach for incorporating prior knowledge of spine anatomy A Sekuboyina, M Rempfler, A Valentinitsch, BH Menze, JS Kirschke Radiology: Artificial Intelligence 2 (2), e190074, 2020 | 39* | 2020 |
| Deep reinforcement learning for organ localization in CT F Navarro, A Sekuboyina, D Waldmannstetter, JC Peeken, SE Combs, ... Medical Imaging with Deep Learning, 544-554, 2020 | 38 | 2020 |
| Red-GAN: Attacking class imbalance via conditioned generation. Yet another medical imaging perspective. AB Qasim, I Ezhov, S Shit, O Schoppe, JC Paetzold, A Sekuboyina, ... Medical Imaging with Deep Learning, 655-668, 2020 | 38 | 2020 |
| A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data H Liebl, D Schinz, A Sekuboyina, L Malagutti, MT Löffler, A Bayat, ... Scientific Data 8 (1), 284, 2021 | 34 | 2021 |
| Grading loss: a fracture grade-based metric loss for vertebral fracture detection M Husseini, A Sekuboyina, M Loeffler, F Navarro, BH Menze, JS Kirschke Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 21 | 2020 |
| Evaluating the Robustness of Self-Supervised Learning in Medical Imaging F Navarro, C Watanabe, S Shit, A Sekuboyina, JC Peeken, SE Combs, ... arXiv preprint arXiv:2105.06986, 2021 | 18 | 2021 |
| Automated opportunistic osteoporosis screening in routine computed tomography of the spine: comparison with dedicated quantitative CT N Sollmann, MT Löffler, M El Husseini, A Sekuboyina, M Dieckmeyer, ... Journal of Bone and Mineral Research 37 (7), 1287-1296, 2022 | 14 | 2022 |
| Automated detection of the contrast phase in MDCT by an artificial neural network improves the accuracy of opportunistic bone mineral density measurements S Rühling, F Navarro, A Sekuboyina, M El Husseini, T Baum, B Menze, ... European Radiology, 1-10, 2022 | 13 | 2022 |
| Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting CM Pirk, PA Gómez, I Lipp, G Buonincontri, M Molina-Romero, ... Medical Imaging with Deep Learning, 638-654, 2020 | 13 | 2020 |
Bjoern H MenzeUniversität ZürichBestätigte E-Mail-Adresse bei uzh.ch
