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Johannes Uln
Johannes Uln
Eigenvision AB
Verified email at maths.lth.se - Homepage
Title
Cited by
Cited by
Year
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 Uln, MH Poulsen, ...
European journal of radiology 113, 89-95, 2019
1262019
Hep-2 staining pattern classification
P Strandmark, J Uln, F Kahl
International Conference on Pattern Recognition (ICPR), 33-36, 2012
932012
In Defense of 3D-Label Stereo
C Olsson, J Uln, Y Boykov
Conference on Computer Vision and Pattern Recognition (CVPR), 2013
812013
RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology
E Trgrdh, P Borrelli, R Kaboteh, T Gillberg, J Uln, O Enqvist, ...
EJNMMI physics 7, 1-12, 2020
792020
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 Uln, M Ohlsson, ...
Clinical physiology and functional imaging 40 (2), 106-113, 2020
522020
Shortest Paths with Higher-Order Regularization
J Uln, P Strandmark, F Kahl
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
492015
An Efficient Optimization Framework for Multi-Region Segmentation based on Lagrangian Duality
J Uln, P Strandmark, F Kahl
IEEE Transactions on Medical Imaging, 2013
492013
Artificial intelligence-aided CT segmentation for body composition analysis: a validation study
P Borrelli, R Kaboteh, O Enqvist, J Uln, E Trgrdh, H Kjlhede, ...
European Radiology Experimental 5, 1-6, 2021
382021
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 Uln, ...
Clinical physiology and functional imaging 39 (6), 399-406, 2019
312019
Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival
P Borrelli, M Larsson, J Uln, O Enqvist, E Trgrdh, MH Poulsen, ...
Clinical Physiology and Functional Imaging 41 (1), 62-67, 2021
302021
Shortest Paths with Curvature and Torsion
P Strandmark, J Uln, F Kahl, L Grady
International Conference on Computer Vision (ICCV), 2013
262013
AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients
P Borrelli, J Ly, R Kaboteh, J Uln, O Enqvist, E Trgrdh, L Edenbrandt
EJNMMI physics 8, 1-11, 2021
242021
Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians
E Trgrdh, O Enqvist, J Uln, E Hvittfeldt, S Garpered, SL Belal, A Bjartell, ...
European Journal of Nuclear Medicine and Molecular Imaging 49 (10), 3412-3418, 2022
222022
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 Uln, E Trgrdh
Clinical physiology and functional imaging 39 (1), 78-84, 2019
222019
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 Uln, A Wittrup, M Bjurberg, E Trgrdh
Clinical and Translational Radiation Oncology 25, 37-45, 2020
202020
Partial Enumeration and Curvature Regularization
C Olsson, J Uln, Y Boykov, V Kolmogorov
International Conference on Computer Vision (ICCV), 2013
202013
Good Features for Reliable Registration in Multi-Atlas Segmentation.
F Kahl, J Alvn, O Enqvist, F Fejne, J Uln, J Fredriksson, M Landgren, ...
VISCERAL Challenge@ ISBI, 12-17, 2015
192015
Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [18F]-PSMA-1007 PET-CT
E Trgrdh, O Enqvist, J Uln, J Jgi, U Bitzn, F Hedeer, K Valind, ...
Diagnostics 12 (9), 2101, 2022
172022
Variability in reference levels for Deauville classifications applied to lymphoma patients examined with 18F-FDG-PET/CT
M Sadik, E Lind, O Enqvist, J Uln, E Polymeri, E Trgrdh, L Edenbrandt
European Journal of Nuclear Medicine and Molecular Imaging 44, 2017
162017
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 Uln, E Polymeri, E Trgrdh, L Edenbrandt
European Journal of Nuclear Medicine and Molecular Imaging 44 (supplement 2), 2017
162017
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