Johannes Ulén

707 total citations
29 papers, 467 citations indexed

About

Johannes Ulén is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Johannes Ulén has authored 29 papers receiving a total of 467 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Radiology, Nuclear Medicine and Imaging, 14 papers in Pulmonary and Respiratory Medicine and 4 papers in Biomedical Engineering. Recurrent topics in Johannes Ulén's work include Radiomics and Machine Learning in Medical Imaging (18 papers), Medical Imaging Techniques and Applications (13 papers) and Prostate Cancer Treatment and Research (8 papers). Johannes Ulén is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (18 papers), Medical Imaging Techniques and Applications (13 papers) and Prostate Cancer Treatment and Research (8 papers). Johannes Ulén collaborates with scholars based in Sweden, Denmark and India. Johannes Ulén's co-authors include Olof Enqvist, Elin Trägårdh, Lars Edenbrandt, Reza Kaboteh, Pablo Borrelli, May Sadik, Poul Flemming Høilund‐Carlsen, Mads Hvid Poulsen, Jane Angel Simonsen and Henrik Kjölhede and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Pattern Recognition Letters.

In The Last Decade

Johannes Ulén

28 papers receiving 460 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Johannes Ulén Sweden 13 330 183 127 51 37 29 467
Faraz Farhadi United States 13 321 1.0× 76 0.4× 219 1.7× 46 0.9× 12 0.3× 36 481
Reza Kaboteh Sweden 13 363 1.1× 317 1.7× 103 0.8× 33 0.6× 34 0.9× 22 529
Tobias Hepp Germany 12 263 0.8× 69 0.4× 86 0.7× 89 1.7× 16 0.4× 26 405
Claudia Ortega Canada 12 222 0.7× 162 0.9× 56 0.4× 13 0.3× 16 0.4× 52 435
Artit Jirapatnakul United States 15 443 1.3× 383 2.1× 110 0.9× 66 1.3× 9 0.2× 42 611
Masahisa Onoguchi Japan 14 471 1.4× 118 0.6× 241 1.9× 13 0.3× 21 0.6× 94 626
Thomas Weißmann Germany 13 210 0.6× 143 0.8× 42 0.3× 49 1.0× 8 0.2× 41 468
Aaron Nelson United States 9 225 0.7× 126 0.7× 34 0.3× 10 0.2× 19 0.5× 22 310
Beibei Jiang China 14 378 1.1× 260 1.4× 126 1.0× 47 0.9× 13 0.4× 33 582
Takuji Kiryu Japan 10 109 0.3× 224 1.2× 33 0.3× 34 0.7× 35 0.9× 32 349

Countries citing papers authored by Johannes Ulén

Since Specialization
Citations

This map shows the geographic impact of Johannes Ulén's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Johannes Ulén with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johannes Ulén more than expected).

Fields of papers citing papers by Johannes Ulén

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Johannes Ulén. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Johannes Ulén. The network helps show where Johannes Ulén may publish in the future.

Co-authorship network of co-authors of Johannes Ulén

This figure shows the co-authorship network connecting the top 25 collaborators of Johannes Ulén. A scholar is included among the top collaborators of Johannes Ulén based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Johannes Ulén. Johannes Ulén is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ulén, Johannes, et al.. (2025). AI based automatic measurement of split renal function in [18F]PSMA-1007 PET/CT. PubMed. 9(1). 20–20. 1 indexed citations
2.
Trägårdh, Elin, Johannes Ulén, Olof Enqvist, et al.. (2025). A fully automated AI-based method for tumour detection and quantification on [18F]PSMA-1007 PET–CT images in prostate cancer. EJNMMI Physics. 12(1). 78–78.
3.
Ying, Thomas, Pablo Borrelli, Lars Edenbrandt, et al.. (2024). AI-based fully automatic image analysis: Optimal abdominal and thoracic segmentation volumes for estimating total muscle volume on computed tomography scans. SHILAP Revista de lepidopterología. 10(2). 78–83. 1 indexed citations
4.
Trägårdh, Elin, Johannes Ulén, Olof Enqvist, Lars Edenbrandt, & Måns Larsson. (2024). Improving sensitivity through data augmentation with synthetic lymph node metastases for AI‐based analysis of PSMA PET‐CT images. Clinical Physiology and Functional Imaging. 44(4). 332–339. 1 indexed citations
5.
Sachpekidis, Christos, Olof Enqvist, Johannes Ulén, et al.. (2024). Artificial intelligence–based, volumetric assessment of the bone marrow metabolic activity in [18F]FDG PET/CT predicts survival in multiple myeloma. European Journal of Nuclear Medicine and Molecular Imaging. 51(8). 2293–2307. 8 indexed citations
6.
Trägårdh, Elin, Olof Enqvist, Johannes Ulén, et al.. (2022). Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [18F]-PSMA-1007 PET-CT. Diagnostics. 12(9). 2101–2101. 24 indexed citations
7.
Trägårdh, Elin, et al.. (2022). Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians. European Journal of Nuclear Medicine and Molecular Imaging. 49(10). 3412–3418. 23 indexed citations
8.
Sadik, May, Johannes Ulén, Olof Enqvist, et al.. (2022). Artificial Intelligence Increases the Agreement among Physicians Classifying Focal Skeleton/Bone Marrow Uptake in Hodgkin’s Lymphoma Patients Staged with [18F]FDG PET/CT—a Retrospective Study. Nuclear Medicine and Molecular Imaging. 57(2). 110–116. 4 indexed citations
9.
Saito, Shintaro, Kenichi Nakajima, Lars Edenbrandt, et al.. (2021). Convolutional neural network-based automatic heart segmentation and quantitation in 123I-metaiodobenzylguanidine SPECT imaging. EJNMMI Research. 11(1). 105–105. 4 indexed citations
10.
Molnár, Dávid, Olof Enqvist, Johannes Ulén, et al.. (2021). Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies. Scientific Reports. 11(1). 23905–23905. 8 indexed citations
11.
Borrelli, Pablo, Reza Kaboteh, Johannes Ulén, et al.. (2021). AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients. EJNMMI Physics. 8(1). 32–32. 21 indexed citations
12.
Borrelli, Pablo, Reza Kaboteh, Olof Enqvist, et al.. (2021). Artificial intelligence-aided CT segmentation for body composition analysis: a validation study. European Radiology Experimental. 5(1). 11–11. 35 indexed citations
13.
Trägårdh, Elin, Pablo Borrelli, Reza Kaboteh, et al.. (2020). RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology. EJNMMI Physics. 7(1). 51–51. 66 indexed citations
14.
Borrelli, Pablo, Måns Larsson, Johannes Ulén, et al.. (2020). Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival. Clinical Physiology and Functional Imaging. 41(1). 62–67. 19 indexed citations
15.
Sadik, May, Reza Kaboteh, Pablo Borrelli, et al.. (2019). Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival. Clinical Physiology and Functional Imaging. 40(2). 106–113. 30 indexed citations
16.
Borrelli, Pablo, Mads Hvid Poulsen, Oke Gerke, et al.. (2019). Artificial intelligence‐based versus manual assessment of prostate cancer in the prostate gland: a method comparison study. Clinical Physiology and Functional Imaging. 39(6). 399–406. 23 indexed citations
17.
Borrelli, Pablo, Olof Enqvist, Johannes Ulén, et al.. (2018). Artificial Intelligence Based Method for Automated PET/CT Measurements of Prostate Gland Volume and Choline Uptake. University of Southern Denmark Research Portal (University of Southern Denmark). 1 indexed citations
18.
19.
Kahl, Fredrik, et al.. (2015). Good Features for Reliable Registration in Multi-Atlas Segmentation. Chalmers Research (Chalmers University of Technology). 1390. 12–17. 9 indexed citations
20.
Backman, Sofia, et al.. (2013). Exploratory study of EEG burst characteristics in preterm infants. PubMed. 11. 4295–4298. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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