| Deep learning for segmentation of 49 selected bones in CT scans: first step in automated PET/CT-based 3D quantification of skeletal metastases SL Belal, M Sadik, R Kaboteh, O Enqvist, J Ulén, MH Poulsen, ... European journal of radiology 113, 89-95, 2019 | 135 | 2019 |
| Hep-2 staining pattern classification P Strandmark, J Ulén, F Kahl International Conference on Pattern Recognition (ICPR), 33-36, 2012 | 95 | 2012 |
| RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology E Trägårdh, P Borrelli, R Kaboteh, T Gillberg, J Ulén, O Enqvist, ... EJNMMI physics 7, 1-12, 2020 | 93 | 2020 |
| In Defense of 3D-Label Stereo C Olsson, J Ulén, Y Boykov Conference on Computer Vision and Pattern Recognition (CVPR), 2013 | 83 | 2013 |
| Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival E Polymeri, M Sadik, R Kaboteh, P Borrelli, O Enqvist, J Ulén, M Ohlsson, ... Clinical physiology and functional imaging 40 (2), 106-113, 2020 | 55 | 2020 |
| Artificial intelligence-aided CT segmentation for body composition analysis: a validation study P Borrelli, R Kaboteh, O Enqvist, J Ulén, E Trägårdh, H Kjölhede, ... European Radiology Experimental 5, 1-6, 2021 | 49 | 2021 |
| Shortest Paths with Higher-Order Regularization J Ulén, P Strandmark, F Kahl IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 | 49 | 2015 |
| An Efficient Optimization Framework for Multi-Region Segmentation based on Lagrangian Duality J Ulén, P Strandmark, F Kahl IEEE Transactions on Medical Imaging, 2013 | 49 | 2013 |
| Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival P Borrelli, M Larsson, J Ulén, O Enqvist, E Trägårdh, MH Poulsen, ... Clinical Physiology and Functional Imaging 41 (1), 62-67, 2021 | 34 | 2021 |
| Artificial intelligence‐based versus manual assessment of prostate cancer in the prostate gland: a method comparison study MA Mortensen, P Borrelli, MH Poulsen, O Gerke, O Enqvist, J Ulén, ... Clinical physiology and functional imaging 39 (6), 399-406, 2019 | 32 | 2019 |
| AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients P Borrelli, J Ly, R Kaboteh, J Ulén, O Enqvist, E Trägårdh, L Edenbrandt EJNMMI physics 8, 1-11, 2021 | 31 | 2021 |
| Shortest Paths with Curvature and Torsion P Strandmark, J Ulén, F Kahl, L Grady International Conference on Computer Vision (ICCV), 2013 | 26 | 2013 |
| Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth H Sartor, D Minarik, O Enqvist, J Ulén, A Wittrup, M Bjurberg, E Trägårdh Clinical and Translational Radiation Oncology 25, 37-45, 2020 | 25 | 2020 |
| Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians E Trägårdh, O Enqvist, J Ulén, E Hvittfeldt, S Garpered, SL Belal, A Bjartell, ... European Journal of Nuclear Medicine and Molecular Imaging 49 (10), 3412-3418, 2022 | 23 | 2022 |
| Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG‐PET/CT in Hodgkin and non‐Hodgkin … M Sadik, E Lind, E Polymeri, O Enqvist, J Ulén, E Trägårdh Clinical physiology and functional imaging 39 (1), 78-84, 2019 | 23 | 2019 |
| Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [18F]-PSMA-1007 PET-CT E Trägårdh, O Enqvist, J Ulén, J Jögi, U Bitzén, F Hedeer, K Valind, ... Diagnostics 12 (9), 2101, 2022 | 22 | 2022 |
| Partial Enumeration and Curvature Regularization C Olsson, J Ulén, Y Boykov, V Kolmogorov International Conference on Computer Vision (ICCV), 2013 | 20 | 2013 |
| Good Features for Reliable Registration in Multi-Atlas Segmentation. F Kahl, J Alvén, O Enqvist, F Fejne, J Ulén, J Fredriksson, M Landgren, ... VISCERAL Challenge@ ISBI, 12-17, 2015 | 19 | 2015 |
| Variability in reference levels for Deauville classifications applied to lymphoma patients examined with 18F-FDG-PET/CT M Sadik, E Lind, O Enqvist, J Ulén, E Polymeri, E Trägårdh, L Edenbrandt European Journal of Nuclear Medicine and Molecular Imaging 44, 2017 | 18 | 2017 |
| Automated quantification of reference levels in liver and mediastinum (blood pool) for the Deauville therapy response classification using FDG-PET/CT in lymphoma patients E Lind, M Sadik, O Enqvist, J Ulén, E Polymeri, E Trägårdh, L Edenbrandt European Journal of Nuclear Medicine and Molecular Imaging 44 (supplement 2), 2017 | 18 | 2017 |
Olof EnqvistAssistant Professor, Department of Signals and Systems, Chalmers University of Technology, Göteborgchalmers.se üzerinde doğrulanmış e-posta adresine sahip