Michael Perkuhn

1.3k total citations
18 papers, 958 citations indexed

About

Michael Perkuhn is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Neurology. According to data from OpenAlex, Michael Perkuhn has authored 18 papers receiving a total of 958 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Radiology, Nuclear Medicine and Imaging, 4 papers in Genetics and 4 papers in Neurology. Recurrent topics in Michael Perkuhn's work include Radiomics and Machine Learning in Medical Imaging (8 papers), Glioma Diagnosis and Treatment (4 papers) and MRI in cancer diagnosis (3 papers). Michael Perkuhn is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (8 papers), Glioma Diagnosis and Treatment (4 papers) and MRI in cancer diagnosis (3 papers). Michael Perkuhn collaborates with scholars based in Germany, United States and Finland. Michael Perkuhn's co-authors include Frank Thiele, Jan Borggrefe, Kai Roman Laukamp, Georgy Shakirin, David Zopfs, Volkmar Schulz, Marco Timmer, Christoph Kabbasch, David Maintz and Alexandra Buhl and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Medical Imaging and Physics in Medicine and Biology.

In The Last Decade

Michael Perkuhn

18 papers receiving 936 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Perkuhn Germany 16 630 185 165 163 132 18 958
Fulvio Zaccagna Italy 22 824 1.3× 258 1.4× 497 3.0× 45 0.3× 153 1.2× 83 1.6k
Leonard Sunwoo South Korea 21 695 1.1× 175 0.9× 303 1.8× 31 0.2× 180 1.4× 72 1.2k
Yoshiharu Higashida Japan 16 420 0.7× 161 0.9× 222 1.3× 86 0.5× 94 0.7× 82 865
Claes Nøhr Ladefoged Denmark 18 788 1.3× 199 1.1× 56 0.3× 112 0.7× 48 0.4× 50 920
Hesheng Wang United States 18 627 1.0× 255 1.4× 238 1.4× 341 2.1× 84 0.6× 77 1.1k
Kristof Baete Belgium 18 664 1.1× 164 0.9× 68 0.4× 99 0.6× 191 1.4× 48 1.0k
Carina Marí Aparici United States 18 593 0.9× 101 0.5× 356 2.2× 35 0.2× 152 1.2× 85 1.3k
Hidetaka Arimura Japan 19 819 1.3× 223 1.2× 547 3.3× 181 1.1× 51 0.4× 123 1.3k
Jan Petr Germany 20 714 1.1× 95 0.5× 276 1.7× 41 0.3× 82 0.6× 84 1.1k
Ananth J. Madhuranthakam United States 25 1.3k 2.1× 129 0.7× 315 1.9× 19 0.1× 57 0.4× 90 1.8k

Countries citing papers authored by Michael Perkuhn

Since Specialization
Citations

This map shows the geographic impact of Michael Perkuhn'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 Michael Perkuhn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Perkuhn more than expected).

Fields of papers citing papers by Michael Perkuhn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Perkuhn. 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 Michael Perkuhn. The network helps show where Michael Perkuhn may publish in the future.

Co-authorship network of co-authors of Michael Perkuhn

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Perkuhn. A scholar is included among the top collaborators of Michael Perkuhn 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 Michael Perkuhn. Michael Perkuhn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Pennig, Lenhard, Rahil Shahzad, Liliana Caldeira, et al.. (2021). Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model. American Journal of Neuroradiology. 42(4). 655–662. 28 indexed citations
2.
Pennig, Lenhard, Alexandra Krauskopf, Rahil Shahzad, et al.. (2021). Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage. Neuroradiology. 63(12). 1985–1994. 15 indexed citations
3.
He, Kan, Xiaoming Liu, Rahil Shahzad, et al.. (2021). Advanced Deep Learning Approach to Automatically Segment Malignant Tumors and Ablation Zone in the Liver With Contrast-Enhanced CT. Frontiers in Oncology. 11. 669437–669437. 14 indexed citations
4.
Jünger, Stephanie T., Kai Roman Laukamp, Lukas Goertz, et al.. (2021). Fully Automated MR Detection and Segmentation of Brain Metastases in Non‐small Cell Lung Cancer Using Deep Learning. Journal of Magnetic Resonance Imaging. 54(5). 1608–1622. 28 indexed citations
5.
Shahzad, Rahil, Lenhard Pennig, Lukas Goertz, et al.. (2020). Fully automated detection and segmentation of intracranial aneurysms in subarachnoid hemorrhage on CTA using deep learning. Scientific Reports. 10(1). 21799–21799. 51 indexed citations
6.
Pennig, Lenhard, Frank Thiele, Lukas Goertz, et al.. (2020). Comparison of Accuracy of Arrival-Time-Insensitive and Arrival-Time-Sensitive CTP Algorithms for Prediction of Infarct Tissue Volumes. Scientific Reports. 10(1). 9252–9252. 4 indexed citations
7.
Laukamp, Kai Roman, Lenhard Pennig, Frank Thiele, et al.. (2020). Automated Meningioma Segmentation in Multiparametric MRI. Clinical Neuroradiology. 31(2). 357–366. 42 indexed citations
8.
Pennig, Lenhard, Lukas Goertz, Rahil Shahzad, et al.. (2020). Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning. Journal of Magnetic Resonance Imaging. 53(1). 259–268. 18 indexed citations
9.
Laukamp, Kai Roman, Georgy Shakirin, Bettina Baeßler, et al.. (2019). Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading. World Neurosurgery. 132. e366–e390. 58 indexed citations
10.
Perkuhn, Michael, Pantelis Stavrinou, Frank Thiele, et al.. (2018). Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine. Investigative Radiology. 53(11). 647–654. 50 indexed citations
11.
Laukamp, Kai Roman, Frank Thiele, Georgy Shakirin, et al.. (2018). Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI. European Radiology. 29(1). 124–132. 138 indexed citations
12.
Boor, Peter, Michael Perkuhn, Ina V. Martin, et al.. (2015). Diffusion‐weighted MRI does not reflect kidney fibrosis in a rat model of fibrosis. Journal of Magnetic Resonance Imaging. 42(4). 990–998. 43 indexed citations
13.
Weissler, Bjoern, Pierre Gebhardt, Peter Michael Dueppenbecker, et al.. (2015). A Digital Preclinical PET/MRI Insert and Initial Results. IEEE Transactions on Medical Imaging. 34(11). 2258–2270. 86 indexed citations
14.
Weissler, Bjoern, Pierre Gebhardt, Christoph Lerche, et al.. (2014). MR compatibility aspects of a silicon photomultiplier-based PET/RF insert with integrated digitisation. Physics in Medicine and Biology. 59(17). 5119–5139. 50 indexed citations
15.
Hadizadeh, Dariusch R., Gregor Jošt, Hubertus Pietsch, et al.. (2014). Intraindividual Quantitative and Qualitative Comparison of Gadopentetate Dimeglumine and Gadobutrol in Time-Resolved Contrast-Enhanced 4-Dimensional Magnetic Resonance Angiography in Minipigs. Investigative Radiology. 49(7). 457–464. 15 indexed citations
16.
Schulz, Volkmar, I. Torres-Espallardó, Steffen Renisch, et al.. (2010). Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data. European Journal of Nuclear Medicine and Molecular Imaging. 38(1). 138–152. 245 indexed citations
17.
Mischke, Karl, Markus Zarse, Michael Perkuhn, et al.. (2005). Telephonic transmission of 12-lead electrocardiograms during acute myocardial infarction. Journal of Telemedicine and Telecare. 11(4). 185–190. 15 indexed citations
18.
Schmidt, R., et al.. (2005). Wearable approach for continuous ECG - and activity patient-monitoring. PubMed. 3. 2184–2187. 58 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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