Kersten Petersen

1.6k total citations · 2 hit papers
13 papers, 951 citations indexed

About

Kersten Petersen is a scholar working on Biomedical Engineering, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Kersten Petersen has authored 13 papers receiving a total of 951 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Biomedical Engineering, 5 papers in Computer Vision and Pattern Recognition and 5 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Kersten Petersen's work include Medical Image Segmentation Techniques (4 papers), Advanced X-ray and CT Imaging (4 papers) and Coronary Interventions and Diagnostics (3 papers). Kersten Petersen is often cited by papers focused on Medical Image Segmentation Techniques (4 papers), Advanced X-ray and CT Imaging (4 papers) and Coronary Interventions and Diagnostics (3 papers). Kersten Petersen collaborates with scholars based in Denmark, United States and United Kingdom. Kersten Petersen's co-authors include Mads Nielsen, Christian Igel, François Lauze, Erik B. Dam, Martin Lillholm, Rikke Rass Winkel, Celine M. Vachon, Katharina Holland, Michiel Kallenberg and Andrew Y. Ng and has published in prestigious journals such as Computer Methods in Applied Mechanics and Engineering, IEEE Transactions on Medical Imaging and Medical Image Analysis.

In The Last Decade

Kersten Petersen

13 papers receiving 917 citations

Hit Papers

Deep Feature Learning for Knee Cartilage Segmentation Usi... 2013 2026 2017 2021 2013 2016 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network
    2013 394 citations Kersten Petersen, Christian Igel et al. Lecture notes in computer science profile →
  2. Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring
    2016 319 citations Michiel Kallenberg, Kersten Petersen et al. IEEE Transactions on Medical Imaging profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kersten Petersen Denmark 8 449 404 257 185 173 13 951
Theresa Thai United States 12 519 1.2× 331 0.8× 170 0.7× 164 0.9× 126 0.7× 32 1.0k
Michał Byra Poland 15 817 1.8× 723 1.8× 220 0.9× 191 1.0× 65 0.4× 42 1.3k
Kelei He China 16 580 1.3× 369 0.9× 441 1.7× 210 1.1× 135 0.8× 25 1.1k
Shekhar S. Chandra Australia 15 347 0.8× 146 0.4× 192 0.7× 244 1.3× 91 0.5× 70 876
Mainak Biswas India 14 470 1.0× 305 0.8× 416 1.6× 121 0.7× 233 1.3× 41 1.4k
Wenjian Qin China 17 562 1.3× 372 0.9× 330 1.3× 212 1.1× 124 0.7× 70 1.0k
M. Muthu Rama Krishnan India 20 443 1.0× 463 1.1× 309 1.2× 71 0.4× 134 0.8× 26 1.1k
Dibendu Betal United Kingdom 11 360 0.8× 764 1.9× 571 2.2× 50 0.3× 166 1.0× 29 1.2k
Lalit Mohan Aggarwal India 11 277 0.6× 169 0.4× 277 1.1× 177 1.0× 92 0.5× 45 878

Countries citing papers authored by Kersten Petersen

Since Specialization
Citations

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

Fields of papers citing papers by Kersten Petersen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kersten Petersen

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

All Works

13 of 13 papers shown
1.
Schuh, Andreas, A. Karmakar, Kersten Petersen, et al.. (2024). Morphology-Based Non-Rigid Registration of Coronary Computed Tomography and Intravascular Images Through Virtual Catheter Path Optimization. IEEE Transactions on Medical Imaging. 44(2). 880–890. 1 indexed citations
2.
Narula, Jagat, Thomas Stuckey, Gaku Nakazawa, et al.. (2024). Prospective deep learning–based quantitative assessment of coronary plaque by computed tomography angiography compared with intravascular ultrasound: the REVEALPLAQUE study. European Heart Journal - Cardiovascular Imaging. 25(9). 1287–1295. 23 indexed citations
3.
Narula, Jagat, Thomas Stuckey, Gaku Nakazawa, et al.. (2023). Primary Results Of The REVEALPLAQUE Study: A Prospective Quantitative Assessment Of AI-based CCTA Plaque Volume Compared With IVUS. Journal of cardiovascular computed tomography. 17(4). S39–S39. 2 indexed citations
4.
Taylor, Charles A., Kersten Petersen, Nan Xiao, et al.. (2023). Patient-specific modeling of blood flow in the coronary arteries. Computer Methods in Applied Mechanics and Engineering. 417. 116414–116414. 29 indexed citations
5.
Sinclair, Matthew, Andreas Schuh, Kersten Petersen, et al.. (2022). Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks. Medical Image Analysis. 78. 102383–102383. 18 indexed citations
6.
Petersen, Kersten, et al.. (2019). TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors. IEEE Transactions on Medical Imaging. 38(11). 2596–2606. 75 indexed citations
7.
Petersen, Kersten, et al.. (2019). Template Transformer Networks for Image Segmentation. 3 indexed citations
8.
Winkel, Rikke Rass, My von Euler‐Chelpin, Mads Nielsen, et al.. (2016). Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case–control study. BMC Cancer. 16(1). 414–414. 35 indexed citations
9.
Kallenberg, Michiel, Kersten Petersen, Mads Nielsen, et al.. (2016). Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring. IEEE Transactions on Medical Imaging. 35(5). 1322–1331. 319 indexed citations breakdown →
10.
Petersen, Kersten, et al.. (2013). Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network. Lecture notes in computer science. 16(Pt 2). 246–253. 394 indexed citations breakdown →
11.
Petersen, Kersten, et al.. (2012). A unifying framework for automatic and semi-automatic segmentation of vertebrae from radiographs using sample-driven active shape models. Machine Vision and Applications. 24(7). 1421–1434. 7 indexed citations
12.
Petersen, Kersten, et al.. (2011). A Bayesian Framework for Automated Cardiovascular Risk Scoring on Standard Lumbar Radiographs. IEEE Transactions on Medical Imaging. 31(3). 663–676. 7 indexed citations
13.
Jäger, Anna K., et al.. (2008). Isolation of linoleic and α‐linolenic acids as COX‐1 and ‐2 inhibitors in rose hip. Phytotherapy Research. 22(7). 982–984. 38 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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