Ullrich Köthe

3.7k total citations · 1 hit paper
39 papers, 1.6k citations indexed

About

Ullrich Köthe is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Ullrich Köthe has authored 39 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 13 papers in Artificial Intelligence and 5 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Ullrich Köthe's work include Medical Image Segmentation Techniques (10 papers), Digital Image Processing Techniques (8 papers) and Gaussian Processes and Bayesian Inference (6 papers). Ullrich Köthe is often cited by papers focused on Medical Image Segmentation Techniques (10 papers), Digital Image Processing Techniques (8 papers) and Gaussian Processes and Bayesian Inference (6 papers). Ullrich Köthe collaborates with scholars based in Germany, United States and Netherlands. Ullrich Köthe's co-authors include Fred A. Hamprecht, Christoph Straehle, Christoph Sommer, Stefan T. Radev, Lynton Ardizzone, Andreas Voß, Ulf K. Mertens, Bernhard Y. Renard, Marc Kirchner and Thorsten Beier and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Ullrich Köthe

36 papers receiving 1.5k citations

Hit Papers

Ilastik: Interactive learning and segmentation toolkit 2011 2026 2016 2021 2011 250 500 750
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. Ilastik: Interactive learning and segmentation toolkit
    2011 793 citations Christoph Straehle, Ullrich Köthe et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ullrich Köthe Germany 17 451 409 299 209 187 39 1.6k
Zeyun Yu United States 26 478 1.1× 138 0.3× 473 1.6× 174 0.8× 218 1.2× 78 2.0k
Min Xu United States 24 527 1.2× 152 0.4× 206 0.7× 313 1.5× 92 0.5× 159 1.8k
Bernhard X. Kausler Germany 6 781 1.7× 645 1.6× 153 0.5× 130 0.6× 59 0.3× 6 2.1k
Tim Becker Germany 15 913 2.0× 865 2.1× 365 1.2× 372 1.8× 173 0.9× 33 2.4k
Bahram Parvin United States 24 586 1.3× 228 0.6× 500 1.7× 533 2.6× 346 1.9× 84 1.8k
Luis Ibáñez United States 18 265 0.6× 346 0.8× 451 1.5× 210 1.0× 559 3.0× 70 2.7k
Hideo Yokota Japan 24 845 1.9× 610 1.5× 309 1.0× 123 0.6× 596 3.2× 175 3.0k
Carsten Marr Germany 27 1.6k 3.5× 543 1.3× 341 1.1× 386 1.8× 193 1.0× 103 3.1k
Yoel Shkolnisky Israel 22 363 0.8× 72 0.2× 367 1.2× 142 0.7× 181 1.0× 54 1.6k
Yichen Wu Taiwan 32 885 2.0× 593 1.4× 469 1.6× 231 1.1× 138 0.7× 121 3.6k

Countries citing papers authored by Ullrich Köthe

Since Specialization
Citations

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

Fields of papers citing papers by Ullrich Köthe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ullrich Köthe

This figure shows the co-authorship network connecting the top 25 collaborators of Ullrich Köthe. A scholar is included among the top collaborators of Ullrich Köthe 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 Ullrich Köthe. Ullrich Köthe 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.
Bürkner, Paul‐Christian, et al.. (2023). Neural superstatistics for Bayesian estimation of dynamic cognitive models. Scientific Reports. 13(1). 13778–13778. 11 indexed citations
2.
Radev, Stefan T., et al.. (2023). BayesFlow: Amortized Bayesian Workflows With NeuralNetworks. The Journal of Open Source Software. 8(89). 5702–5702. 12 indexed citations
3.
Kruse, Jakob, et al.. (2023). Towards learned emulation of interannual water isotopologue variations in General Circulation Models. SHILAP Revista de lepidopterología. 2. 1 indexed citations
4.
Köthe, Ullrich. (2023). A Review of Change of Variable Formulas for Generative Modeling. arXiv (Cornell University). 1 indexed citations
5.
Radev, Stefan T., Frederik Graw, Simiao Chen, et al.. (2021). OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany. PLoS Computational Biology. 17(10). e1009472–e1009472. 16 indexed citations
6.
Ardizzone, Lynton, et al.. (2020). Generative Classifiers as a Basis for Trustworthy Computer Vision.. arXiv (Cornell University). 1 indexed citations
7.
Ardizzone, Lynton, et al.. (2020). Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling.
8.
Radev, Stefan T., Ulf K. Mertens, Andreas Voß, Lynton Ardizzone, & Ullrich Köthe. (2020). BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 33(4). 1452–1466. 101 indexed citations
9.
Kruse, Jakob, et al.. (2019). HINT: Hierarchical Invertible Neural Transport for General and Sequential Bayesian inference.. arXiv (Cornell University). 2 indexed citations
10.
Kleesiek, Jens, Fabian Isensee, Katerina Deike‐Hofmann, et al.. (2019). Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?. Investigative Radiology. 54(10). 653–660. 93 indexed citations
11.
Kleesiek, Jens, Jens Petersen, Markus Döring, et al.. (2016). Virtual Raters for Reproducible and Objective Assessments in Radiology. Scientific Reports. 6(1). 25007–25007. 15 indexed citations
12.
Köthe, Ullrich, Frank Herrmannsdörfer, Ilia Kats, & Fred A. Hamprecht. (2014). SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy. Histochemistry and Cell Biology. 141(6). 613–627. 11 indexed citations
13.
Beier, Thorsten, Thorben Kroeger, Jörg Hendrik Kappes, Ullrich Köthe, & Fred A. Hamprecht. (2014). Cut, Glue, & Cut: A Fast, Approximate Solver for Multicut Partitioning. 73–80. 19 indexed citations
14.
Hanselmann, Michael, et al.. (2012). Active Learning for Convenient Annotation and Classification of Secondary Ion Mass Spectrometry Images. Analytical Chemistry. 85(1). 147–155. 7 indexed citations
15.
Straehle, Christoph, Ullrich Köthe, Graham Knott, & Fred A. Hamprecht. (2011). Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images. Lecture notes in computer science. 14(Pt 1). 653–660. 27 indexed citations
16.
Meine, Hans, et al.. (2008). A topological sampling theorem for Robust boundary reconstruction and image segmentation. Discrete Applied Mathematics. 157(3). 524–541. 12 indexed citations
17.
18.
Cocosco, Chris A., et al.. (2006). Automatic cardiac MRI myocardium segmentation using graphcut. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6144. 61440A–61440A. 18 indexed citations
19.
Köthe, Ullrich, et al.. (2005). Connectivity preserving digitization of blurred binary images in 2D and 3D. Computers & Graphics. 30(1). 70–76. 3 indexed citations
20.
Köthe, Ullrich. (2003). Integrated Edge and Junction Detection with the Boundary Tensor. 424–431. 6 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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