Dr. rer. nat. Fabian Isensee

Head of Applied Computer Vision Lab Support Unit DKFZ

During his PhD at the Division of Medical Image Computing of the German Cancer Research Center, Fabian Isensee has researched deep learning techniques for semantic segmentation of (three-dimensional) datasets in the biological and medical domain. He has been particularly active in the development of methods for automated segmentation pipeline design. His most prominent effort in that regard, nnU-Net, has become the de-facto standard for segmentation in the medical domain and was recently accepted for publication in Nature Methods. Throughout his PhD, Fabian Isensee has consistently enabled the translation of state-of-the-art algorithms into real-world applications. He is furthermore adamant about making his research publicly available. The methods he developed have won multiple international segmentation competitions.

Since 2020, Fabian Isensee is heading the HI Support Unit ‘Applied Computer Vision Lab’ at the DKFZ with the goal of translating state-of-the-art AI methods to the many diverse research applications found across the Helmholtz association. The support unit is working in close collaboration with Helmholtz researchers, provides consulting, develops domain agnostic state-of-the art software for AI-based image analysis and provides solutions for algorithm and competition evaluation.


Roß, T., Bruno, P., Reinke, A., Wiesenfarth, M., Koeppel, L., Full, P. M., Pekdemir, B., Godau, P., Trofimova, D., Isensee, F., Adler, T. J., Tran, T. N., Moccia, S., Calimeri, F., Müller-Stich, B. P., Kopp-Schneider, A., & Maier-Hein, L. (2023). Beyond rankings: Learning (more) from algorithm validation. Medical Image Analysis, 102765. https://doi.org/10.1016/j.media.2023.102765
Reinke, A., Tizabi, M. D., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A. E., Rädsch, T., Sudre, C. H., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Blaschko, M., Büttner, F., Cardoso, M. J., Cheplygina, V., Chen, J., Christodoulou, E., … Maier-Hein, L. (2023). Understanding metric-related pitfalls in image analysis validation. arXiv. http://arxiv.org/abs/2302.01790
Eisenmann, M., Reinke, A., Weru, V., Tizabi, M. D., Isensee, F., Adler, T. J., Godau, P., Cheplygina, V., Kozubek, M., Ali, S., Gupta, A., Kybic, J., Noble, A., de Solórzano, C. O., Pachade, S., Petitjean, C., Sage, D., Wei, D., Wilden, E., … Maier-Hein, L. (2022). Biomedical image analysis competitions: The state of current participation practice. arXiv. https://doi.org/10.48550/arXiv.2212.08568
Roth, H. R., Xu, Z., Tor-Díez, C., Sanchez Jacob, R., Zember, J., Molto, J., Li, W., Xu, S., Turkbey, B., Turkbey, E., Yang, D., Harouni, A., Rieke, N., Hu, S., Isensee, F., Tang, C., Yu, Q., Sölter, J., Zheng, T., … Linguraru, M. G. (2022). Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge. Medical Image Analysis, 82, 102605. https://doi.org/10.1016/j.media.2022.102605
Ostmeier, S., Axelrod, B., Bertels, J., Isensee, F., Lansberg, M. G., Christensen, S., Albers, G. W., Li, L.-J., & Heit, J. J. (2022). USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotations. arXiv. https://doi.org/10.48550/arXiv.2209.13008
Zimmerer, D., Full, P. M., Isensee, F., Jäger, P., Adler, T., Petersen, J., Köhler, G., Ross, T., Reinke, A., Kascenas, A., Jensen, B. S., O’Neil, A. Q., Tan, J., Hou, B., Batten, J., Qiu, H., Kainz, B., Shvetsova, N., Fedulova, I., … Maier-Hein, K. (2022). MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images. IEEE Transactions on Medical Imaging, 41(10), 2728–2738. https://doi.org/10.1109/TMI.2022.3170077
Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M. D., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., Reyes, M., Riegler, M. A., Wiesenfarth, M., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A. E., Rädsch, T., Acion, L., Antonelli, M., … Jäger, P. F. (2022). Metrics reloaded: Pitfalls and recommendations for image analysis validation. arXiv. https://doi.org/10.48550/arXiv.2206.01653
Yang, L., Liu, Q., Kumar, P., Sengupta, A., Farnoud, A., Shen, R., Trofimova, D., Kutschke, D., Piraud, M., Isensee, F., Burgstaller, G., Rehberg, M., Stoeger, T., & Schmid, O. (2022). Multimodal 4D imaging and deep learning unveil acinar migration of tissue-resident, nanoparticle-laden macrophages in the lung. European Respiratory Journal, 60(suppl 66). https://doi.org/10.1183/13993003.congress-2022.407
Isensee, F., Ulrich, C., Wald, T., & Maier-Hein, K. H. (2022). Extending nnU-Net is all you need. arXiv. https://doi.org/10.48550/arXiv.2208.10791
Vollmuth, P., Foltyn, M., Huang, R. Y., Galldiks, N., Petersen, J., Isensee, F., van den Bent, M. J., Barkhof, F., Park, J. E., Park, Y. W., Ahn, S. S., Brugnara, G., Meredig, H., Jain, R., Smits, M., Pope, W. B., Maier-Hein, K., Weller, M., Wen, P. Y., … Bendszus, M. (2022). AI-based decision support improves reproducibility of tumor response assessment in neuro-oncology: an international multi-reader study. Neuro-Oncology, noac189. https://doi.org/10.1093/neuonc/noac189
Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., van Ginneken, B., Bilello, M., Bilic, P., Christ, P. F., Do, R. K. G., Gollub, M. J., Heckers, S. H., Huisman, H., Jarnagin, W. R., … Cardoso, M. J. (2022). The Medical Segmentation Decathlon. Nature Communications, 13(1), 4128. https://doi.org/10.1038/s41467-022-30695-9
Reinke, A., Tizabi, M. D., Sudre, C. H., Eisenmann, M., Rädsch, T., Baumgartner, M., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Bankhead, P., Benis, A., Cardoso, M. J., Cheplygina, V., Christodoulou, E., Cimini, B., Collins, G. S., Farahani, K., van Ginneken, B., … Maier-Hein, L. (2022). Common Limitations of Image Processing Metrics: A Picture Story. arXiv. https://doi.org/10.48550/arXiv.2104.05642
Assalauova, D., Ignatenko, A., Isensee, F., Trofimova, D., & Vartanyants, I. A. (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. https://doi.org/10.1107/S1600576722002667
Schellenberg, M., Dreher, K. K., Holzwarth, N., Isensee, F., Reinke, A., Schreck, N., Seitel, A., Tizabi, M. D., Maier-Hein, L., & Gröhl, J. (2022). Semantic segmentation of multispectral photoacoustic images using deep learning. Photoacoustics, 26, 100341. https://doi.org/10.1016/j.pacs.2022.100341
Yang, L., Shen, R., Trofimova, D., Stöger, T., Piraud, M., Isensee, F., & Schmid, O. (2022). Deep learning in pulmonary drug delivery. ERJ Open Research, 8(suppl 8). https://doi.org/10.1183/23120541.LSC-2022.190
Pflüger, I., Wald, T., Isensee, F., Schell, M., Meredig, H., Schlamp, K., Bernhardt, D., Brugnara, G., Heußel, C. P., Debus, J., Wick, W., Bendszus, M., Maier-Hein, K. H., & Vollmuth, P. (2022). Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks. Neuro-Oncology Advances, 4(1), vdac138. https://doi.org/10.1093/noajnl/vdac138
Roß, T., Reinke, A., Full, P. M., Wagner, M., Kenngott, H., Apitz, M., Hempe, H., Mindroc-Filimon, D., Scholz, P., Tran, T. N., Bruno, P., Arbeláez, P., Bian, G.-B., Bodenstedt, S., Bolmgren, J. L., Bravo-Sánchez, L., Chen, H.-B., González, C., Guo, D., … Maier-Hein, L. (2021). Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge. Medical Image Analysis, 70, 101920. https://doi.org/10.1016/j.media.2020.101920
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211. https://doi.org/10.1038/s41592-020-01008-z
Baumgartner, M., Jäger, P. F., Isensee, F., & Maier-Hein, K. H. (2021). nnDetection: A Self-configuring Method for Medical Object Detection. In M. de Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, & C. Essert (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (pp. 530–539). Springer International Publishing. https://doi.org/10.1007/978-3-030-87240-3_51
Schell, M., Pflüger, I., Brugnara, G., Isensee, F., Neuberger, U., Foltyn, M., Kessler, T., Sahm, F., Wick, A., Nowosielski, M., Heiland, S., Weller, M., Platten, M., Maier-Hein, K. H., Von Deimling, A., Van Den Bent, M. J., Gorlia, T., Wick, W., Bendszus, M., & Kickingereder, P. (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. https://doi.org/10.1093/neuonc/noaa120
Full, P. M., Isensee, F., Jäger, P. F., & Maier-Hein, K. (2020). Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI. arXiv. https://doi.org/10.48550/arXiv.2011.07592
Isensee, F., Jaeger, P. F., Full, P. M., Vollmuth, P., & Maier-Hein, K. H. (2020). nnU-Net for Brain Tumor Segmentation. arXiv. https://doi.org/10.48550/arXiv.2011.00848
Kickingereder, P., Brugnara, G., Hansen, M. B., Nowosielski, M., Pflüger, I., Schell, M., Isensee, F., Foltyn, M., Neuberger, U., Kessler, T., Sahm, F., Wick, A., Heiland, S., Weller, M., Platten, M., von Deimling, A., Maier-Hein, K. H., Østergaard, L., van den Bent, M. J., … Bendszus, M. (2020). Noninvasive Characterization of Tumor Angiogenesis and Oxygenation in Bevacizumab-treated Recurrent Glioblastoma by Using Dynamic Susceptibility MRI: Secondary Analysis of the European Organization for Research and Treatment of Cancer 26101 Trial. Radiology, 297(1), 164–175. https://doi.org/10.1148/radiol.2020200978
Zimmerer, D., Isensee, F., Petersen, J., Kohl, S., & Maier-Hein, K. (2020). Abstract: Unsupervised Anomaly Localization Using Variational Auto-Encoders. In T. Tolxdorff, T. M. Deserno, H. Handels, A. Maier, K. H. Maier-Hein, & C. Palm (Eds.), Bildverarbeitung für die Medizin 2020 (pp. 199–199). Springer Fachmedien. https://doi.org/10.1007/978-3-658-29267-6_43
Laufer, F., Ziegler, S., Schackmar, F., Moreno Viteri, E. A., Götz, M., Debus, C., Isensee, F., & Paetzold, U. W. (n.d.). Process Insights into Perovskite Thin-Film Photovoltaics from Machine Learning with In Situ Luminescence Data. Solar RRL, n/a(n/a), 2201114. https://doi.org/10.1002/solr.202201114