Hep-2 staining pattern classification P Strandmark, J Ulén, F Kahl International Conference on Pattern Recognition (ICPR), 33-36, 2012 | 80 | 2012 |
In Defense of 3D-Label Stereo C Olsson, J Ulén, Y Boykov Conference on Computer Vision and Pattern Recognition (CVPR), 2013 | 57 | 2013 |
Shortest Paths with Higher-Order Regularization J Ulén, P Strandmark, F Kahl IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 | 40 | 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 | 40 | 2013 |
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 | 32 | 2019 |
Shortest Paths with Curvature and Torsion P Strandmark, J Ulén, F Kahl, L Grady International Conference on Computer Vision (ICCV), 2013 | 24 | 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 | 17 | 2015 |
Partial Enumeration and Curvature Regularization C Olsson, J Ulén, Y Boykov, V Kolmogorov International Conference on Computer Vision (ICCV), 2013 | 13* | 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 | 10 | 2020 |
Optimization for multi-region segmentation of cardiac MRI J Ulén, P Strandmark, F Kahl Statistical Atlases and Computational Models of the Heart: Imaging and …, 2012 | 7 | 2012 |
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 | 6 | 2019 |
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 | 5 | 2019 |
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 EANM'17, 2017 | 4 | 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 M Sadik, E Lind, O Enqvist, J Ulén, E Polymeri, E Trägårdh, L Edenbrandt EANM'17, 2017 | 4 | 2017 |
Analytical validation of an automated method for segmentation of the prostate gland in CT images E Polymeri, M Sadik, R Kaboteh, O Enqvist, J Ulén, E Trägårdh, ... EANM'17, 2017 | 4 | 2017 |
Convolutional neural networks for segmentation of 49 selected bones in CT images show high reproducibility M Sadik, R Kaboteh, O Enqvist, J Ulén, E Trägårdh, MH Poulsen, ... European Journal of Nuclear Medicine and Molecular Imaging 44 (Suppl. 2), S352, 2017 | 4 | 2017 |
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), 1-12, 2020 | 3 | 2020 |
Shape-aware label fusion for multi-atlas frameworks J Alvén, F Kahl, M Landgren, V Larsson, J Ulén, O Enqvist Pattern Recognition Letters 124, 109-117, 2019 | 3 | 2019 |
Higher-Order Regularization in Computer Vision J Ulén PhD thesis, Lund University 2014 (7), 2014 | 3 | 2014 |
Exploratory study of EEG burst characteristics in preterm infants Z Simayijiang, S Backman, J Ulén, S Wikström, K Åström Engineering in Medicine and Biology Society (EMBS), 4295-4298, 2013 | 3 | 2013 |