Fabian Isensee

15.6k total citations · 1 hit paper
37 papers, 955 citations indexed

About

Fabian Isensee is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Artificial Intelligence. According to data from OpenAlex, Fabian Isensee has authored 37 papers receiving a total of 955 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Radiology, Nuclear Medicine and Imaging, 11 papers in Biomedical Engineering and 9 papers in Artificial Intelligence. Recurrent topics in Fabian Isensee's work include Glioma Diagnosis and Treatment (7 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Advanced Neural Network Applications (6 papers). Fabian Isensee is often cited by papers focused on Glioma Diagnosis and Treatment (7 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Advanced Neural Network Applications (6 papers). Fabian Isensee collaborates with scholars based in Germany, United States and Switzerland. Fabian Isensee's co-authors include Klaus Maier‐Hein, Heinz‐Peter Schlemmer, Wolfgang Wick, Philipp Kickingereder, Martin Bendszus, Gianluca Brugnara, David Bonekamp, Ulf Neuberger, Sabine Heiland and Marianne Schell and has published in prestigious journals such as Advanced Materials, Journal of Clinical Oncology and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Fabian Isensee

35 papers receiving 933 citations

Hit Papers

align trajectories

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.

Automated brain extraction of multisequence MRI using artificial neural networks

2019 364 citations Fabian Isensee, Marianne Schell et al. profile →

Peers

Fabian Isensee

RNMI

CVPR

NEURO

Yusong Lin China

Fulvio Zaccagna Italy

Leonard Sunwoo South Korea

Robert Gray United Kingdom

Roel Van Holen Belgium

Junfeng Zhang China

Alessandro Crimi Switzerland

Ning Guo United States

M. Bauer Germany

Ingerid Reinertsen Norway

Yusong Lin China

Fabian Isensee

594 ×1.0

RNMI

124 ×0.6

CVPR

93 ×0.4

163 ×1.0

109 ×0.7

NEURO

Citations per year, relative to Fabian Isensee Fabian Isensee (= 1×) peers Yusong Lin

Countries citing papers authored by Fabian Isensee

Since Specialization

Citations

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

Fields of papers citing papers by Fabian Isensee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabian Isensee

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

All Works

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20 of 20 papers shown

Phalempin, Maxime, et al.. (2025). Deep learning segmentation of soil constituents in 3D X-ray CT images. Geoderma. 458. 117321–117321. 2 indexed citations

Wood, Andrew M., Nicholas Heller, Fabian Isensee, et al.. (2024). Fully Automated Versions of Clinically Validated Nephrometry Scores Demonstrate Superior Predictive Utility versus Human Scores. British Journal of Urology. 133(6). 690–698.

Zenk, Maximilian, David Zimmerer, Fabian Isensee, et al.. (2024). Comparative benchmarking of failure detection methods in medical image segmentation: Unveiling the role of confidence aggregation. Medical Image Analysis. 101. 103392–103392. 4 indexed citations

Laufer, Felix, Sebastian Ziegler, Fabian Schackmar, et al.. (2023). Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data. Solar RRL. 7(7). 18 indexed citations

Ziegler, Sebastian, Felix Laufer, Markus Götz, et al.. (2023). Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI. Advanced Materials. 36(7). e2307160–e2307160. 15 indexed citations

Kofler, Florian, Ivan Ezhov, Fabian Isensee, et al.. (2023). Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient. Zurich Open Repository and Archive (University of Zurich). 2(May 2023). 27–71. 14 indexed citations

Godinho, Jose R. A., et al.. (2023). ParticleSeg3D: A scalable out-of-the-box deep learning segmentation solution for individual particle characterization from micro CT images in mineral processing and recycling. Powder Technology. 434. 119286–119286. 6 indexed citations

Kovàcs, Bálint, Michael Baumgartner, Fabian Isensee, et al.. (2023). Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer. Scientific Reports. 13(1). 19805–19805. 1 indexed citations

Ziemons, K., Martin Köcher, Garry Ceccon, et al.. (2023). Automated Brain Tumor Detection and Segmentation for Treatment Response Assessment Using Amino Acid PET. Journal of Nuclear Medicine. 64(10). 1594–1602. 15 indexed citations

10.

Isensee, Fabian, Jeroen Bertels, Michael Mlynash, et al.. (2023). USE-Evaluator: Performance metrics for medical image segmentation models supervised by uncertain, small or empty reference annotations in neuroimaging. Medical Image Analysis. 90. 102927–102927. 10 indexed citations

11.

Roß, Tobias, Annika Reinke, Manuel Wiesenfarth, et al.. (2023). Beyond rankings: Learning (more) from algorithm validation. Medical Image Analysis. 86. 102765–102765. 7 indexed citations

12.

Isensee, Fabian, Marianne Schell, Denise Bernhardt, et al.. (2022). Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks. Neuro-Oncology Advances. 4(1). vdac138–vdac138. 15 indexed citations

13.

Ignatenko, Alexandr, et al.. (2022). Classification of diffraction patterns using a convolutional neural network in single-particle-imaging experiments performed at X-ray free-electron lasers. Journal of Applied Crystallography. 55(3). 444–454. 6 indexed citations

14.

Isensee, Fabian, Annika Reinke, Nicholas Schreck, et al.. (2022). Semantic segmentation of multispectral photoacoustic images using deep learning. Photoacoustics. 26. 100341–100341. 20 indexed citations

15.

Scherer, Moritz, et al.. (2021). Automatic image-based pedicle screw planning. 51–51. 4 indexed citations

16.

Schell, Marianne, Gianluca Brugnara, Fabian Isensee, et al.. (2020). Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial. Neuro-Oncology. 22(11). 1667–1676. 9 indexed citations

17.

Menze, Bjoern, Fabian Isensee, Roland Wiest, et al.. (2020). Analyzing magnetic resonance imaging data from glioma patients using deep learning. Computerized Medical Imaging and Graphics. 88. 101828–101828. 26 indexed citations

18.

Full, Peter M., et al.. (2020). Pretrained nnU-Net Model from the cMRI M&Ms Challenge 2020. Zenodo (CERN European Organization for Nuclear Research).

19.

Brugnara, Gianluca, Fabian Isensee, Ulf Neuberger, et al.. (2020). Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis. European Radiology. 30(4). 2356–2364. 19 indexed citations

20.

Isensee, Fabian, Jens Petersen, Simon Köhl, Paul F. Jäger, & Klaus Maier‐Hein. (2019). nnU-Net: Breaking the Spell on Successful Medical Image Segmentation.. arXiv (Cornell University). 84 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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