Dr. rer. nat. Fabian Isensee

Head of Engineering and Support Unit at 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.

Publications

Isensee, F., Rokuss, M., Krämer, L., Dinkelacker, S., Ravindran, A., Stritzke, F., Hamm, B., Wald, T., Langenberg, M., Ulrich, C., Deissler, J., Floca, R., & Maier-Hein, K. (2025). nnInteractive: Redefining 3D Promptable Segmentation (arXiv:2503.08373). arXiv. https://doi.org/10.48550/arXiv.2503.08373
Bassi, P. R. A. S., Li, W., Tang, Y., Isensee, F., Wang, Z., Chen, J., Chou, Y.-C., Kirchhoff, Y., Rokuss, M., Huang, Z., Ye, J., He, J., Wald, T., Ulrich, C., Baumgartner, M., Roy, S., Maier-Hein, K. H., Jaeger, P., Ye, Y., … Zhou, Z. (2025). Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation? (arXiv:2411.03670). arXiv. https://doi.org/10.48550/arXiv.2411.03670
Isensee, F., Kirchhoff, Y., Kraemer, L., Rokuss, M., Ulrich, C., & Maier-Hein, K. H. (2024). Scaling nnU-Net for CBCT Segmentation (arXiv:2411.17213). arXiv. https://doi.org/10.48550/arXiv.2411.17213
Yang, L., Liu, Q., Kumar, P., Sengupta, A., Farnoud, A., Shen, R., Trofimova, D., Ziegler, S., Davoudi, N., Doryab, A., Yildirim, A. Ö., Diefenbacher, M. E., Schiller, H. B., Razansky, D., Piraud, M., Burgstaller, G., Kreyling, W. G., Isensee, F., Rehberg, M., … Schmid, O. (2024). LungVis 1.0: an automatic AI-powered 3D imaging ecosystem unveils spatial profiling of nanoparticle delivery and acinar migration of lung macrophages. Nature Communications, 15(1), 10138. https://doi.org/10.1038/s41467-024-54267-1
Wald, T., Ulrich, C., Köhler, G., Zimmerer, D., Denner, S., Baumgartner, M., Isensee, F., Jaini, P., & Maier-Hein, K. H. (2024). Decoupling Semantic Similarity from Spatial Alignment for Neural Networks (arXiv:2410.23107). arXiv. https://doi.org/10.48550/arXiv.2410.23107
Klein, L., Ziegler, S., Gerst, F., Morgenroth, Y., Gotkowski, K., Schöniger, E., Heni, M., Kipke, N., Friedland, D., Seiler, A., Geibelt, E., Yamazaki, H., Häring, H. U., Wagner, S., Nadalin, S., Königsrainer, A., Mihaljević, A. L., Hartmann, D., Fend, F., … Wagner, R. (2024). Explainable AI-based analysis of human pancreas sections identifies traits of type 2 diabetes. medRxiv. https://doi.org/10.1101/2024.10.23.24315937
Rokuss, M., Kovacs, B., Kirchhoff, Y., Xiao, S., Ulrich, C., Maier-Hein, K. H., & Isensee, F. (2024). From FDG to PSMA: A Hitchhiker’s Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging (arXiv:2409.09478). arXiv. https://doi.org/10.48550/arXiv.2409.09478
Rokuss, M., Kirchhoff, Y., Roy, S., Kovacs, B., Ulrich, C., Wald, T., Zenk, M., Denner, S., Isensee, F., Vollmuth, P., Kleesiek, J., & Maier-Hein, K. (2024). Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting (arXiv:2409.13416). arXiv. https://doi.org/10.48550/arXiv.2409.13416
Kovacs, B., Xiao, S., Rokuss, M., Ulrich, C., Isensee, F., & Maier-Hein, K. H. (2024). Data-Centric Strategies for Overcoming PET/CT Heterogeneity: Insights from the AutoPET III Lesion Segmentation Challenge (arXiv:2409.10120). arXiv. https://doi.org/10.48550/arXiv.2409.10120
Rädsch, T., Reinke, A., Weru, V., Tizabi, M. D., Heller, N., Isensee, F., Kopp-Schneider, A., & Maier-Hein, L. (2024). Quality Assured: Rethinking Annotation Strategies in Imaging AI (arXiv:2407.17596). arXiv. https://doi.org/10.48550/arXiv.2407.17596
Isensee, F., Wald, T., Ulrich, C., Baumgartner, M., Roy, S., Maier-Hein, K., & Jaeger, P. F. (2024). nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation (arXiv:2404.09556). arXiv. https://doi.org/10.48550/arXiv.2404.09556
Kirchhoff, Y., Rokuss, M. R., Roy, S., Kovacs, B., Ulrich, C., Wald, T., Zenk, M., Vollmuth, P., Kleesiek, J., Isensee, F., & Maier-Hein, K. (2024). Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures (arXiv:2404.03010). arXiv. https://doi.org/10.48550/arXiv.2404.03010
Wood, A. M., Abdallah, N., Heller, N., Benidir, T., Isensee, F., Tejpaul, R., Suk-Ouichai, C., Curry, C., You, A., Remer, E., Haywood, S., Campbell, S., Papanikolopoulos, N., & Weight, C. (2024). Fully Automated Versions of Clinically Validated Nephrometry Scores Demonstrate Superior Predictive Utility versus Human Scores. BJU International, 133(6), 690–698. https://doi.org/10.1111/bju.16276
Yang, L., Kumar, P., Liu, Q., Chen, P., Li, C., Lüth, C., Krämer, L., Gabriel, C., Jeridi, A., Piraud, M., Stoeger, T., Staab-Weijnitz, C. a., Jäger, P., Rehberg, M., Isensee, F., & Schmid, O. (2024). Fresh Perspectives on Lung Morphometry and Pulmonary Drug Delivery: AI-powered 3D Imaging in Healthy and Fibrotic Murine Lungs. In B30. SCARRED FOR LIFE: TRANSLATIONAL RESEARCH IN INTERSTITIAL ABNORMALITIES AND LUNG FIBROSIS (Vol. 1–299, pp. A3242–A3242). American Thoracic Society. https://doi.org/10.1164/ajrccm-conference.2024.209.1_MeetingAbstracts.A3242
Yang, K., Musio, F., Ma, Y., Juchler, N., Paetzold, J. C., Al-Maskari, R., Höher, L., Li, H. B., Hamamci, I. E., Sekuboyina, A., Shit, S., Huang, H., Prabhakar, C., Rosa, E. de la, Waldmannstetter, D., Kofler, F., Navarro, F., Menten, M., Ezhov, I., … Menze, B. (2024). Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA (arXiv:2312.17670). arXiv. https://doi.org/10.48550/arXiv.2312.17670
Ulrich, C., Knobloch, C., Holzschuh, J. C., Wald, T., Rokuss, M. R., Zenk, M., Fischer, M., Baumgartner, M., Isensee, F., & Maier-Hein, K. H. (2024). Mitigating False Predictions In Unreasonable Body Regions (arXiv:2404.15718). arXiv. https://doi.org/10.48550/arXiv.2404.15718
Gutsche, R., Lowis, C., Ziemons, K., Kocher, M., Ceccon, G., Brambilla, C. R., Shah, N. J., Langen, K., Galldiks, N., Isensee, F., & Lohmann, P. (2024). Deep learning-based amino acid PET brain tumor segmentation for automated response assessment in patients with brain tumors. Nuklearmedizin - NuclearMedicine, 63, L53. https://doi.org/10.1055/s-0044-1782321
Gotkowski, K., Lüth, C., Jäger, P. F., Ziegler, S., Krämer, L., Denner, S., Xiao, S., Disch, N., Maier-Hein, K. H., & Isensee, F. (2024). Embarrassingly Simple Scribble Supervision for 3D Medical Segmentation (arXiv:2403.12834). arXiv. https://doi.org/10.48550/arXiv.2403.12834
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., Buettner, F., Cardoso, M. J., Cheplygina, V., Chen, J., Christodoulou, E., Cimini, B. A., … Maier-Hein, L. (2024). Understanding metric-related pitfalls in image analysis validation. Nature Methods, 1–13. https://doi.org/10.1038/s41592-023-02150-0
Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M. D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., Reyes, M., Riegler, M. A., Wiesenfarth, M., Kavur, A. E., Sudre, C. H., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Rädsch, T., … Jäger, P. F. (2024). Metrics reloaded: recommendations for image analysis validation. Nature Methods, 21(2), 195–212. https://doi.org/10.1038/s41592-023-02151-z
Yang, K., Musio, F., Ma, Y., Juchler, N., Paetzold, J. C., Al-Maskari, R., Höher, L., Li, H. B., Hamamci, I. E., Sekuboyina, A., Shit, S., Huang, H., Waldmannstetter, D., Kofler, F., Navarro, F., Menten, M., Ezhov, I., Rueckert, D., Vos, I., … Menze, B. (2024). TopCoW: Benchmarking Topology-Aware Anatomical Segmentation of the Circle of Willis (CoW) for CTA and MRA (arXiv:2312.17670). arXiv. https://doi.org/10.48550/arXiv.2312.17670
Gupta, S., da Assuncao Godinho, J. R., Gotkowski, K., & Isensee, F. (2024). Standardized and semiautomated workflow for 3D characterization of liberated particles. Powder Technology, 433, 119159. https://doi.org/10.1016/j.powtec.2023.119159
Lamm, L., Zufferey, S., Righetto, R. D., Wietrzynski, W., Yamauchi, K. A., Burt, A., Liu, Y., Zhang, H., Martinez-Sanchez, A., Ziegler, S., Isensee, F., Schnabel, J. A., Engel, B. D., & Peng, T. (2024). MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography. bioRxiv. https://doi.org/10.1101/2024.01.05.574336
Gotkowski, K., Gupta, S., Godinho, J. R. A., Tochtrop, C. G. S., Maier-Hein, K. H., & Isensee, F. (2024). 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. https://doi.org/10.1016/j.powtec.2023.119286
Roy, S., Koehler, G., Baumgartner, M., Ulrich, C., Isensee, F., Jaeger, P. F., & Maier-Hein, K. (2024). Abstract: 3D Medical Image Segmentation with Transformer-based Scaling of ConvNets. In A. Maier, T. M. Deserno, H. Handels, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024 (pp. 79–79). Springer Fachmedien. https://doi.org/10.1007/978-3-658-44037-4_23
Koehler, G., Wald, T., Ulrich, C., Zimmerer, D., Jaeger, P. F., Franke, J. K., Kohl, S., Isensee, F., & Maier-Hein, K. H. (2024). RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 810–818. https://openaccess.thecvf.com/content/WACV2024/html/Kohler_RecycleNet_Latent_Feature_Recycling_Leads_to_Iterative_Decision_Refinement_WACV_2024_paper.html
Isensee, F., & Maier-Hein, K. H. (2023). Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans (arXiv:2309.13747). arXiv. https://doi.org/10.48550/arXiv.2309.13747
Kofler, F., Möller, H., Buchner, J. A., de la Rosa, E., Ezhov, I., Rosier, M., Mekki, I., Shit, S., Negwer, M., Al-Maskari, R., Ertürk, A., Vinayahalingam, S., Isensee, F., Pati, S., Rueckert, D., Kirschke, J. S., Ehrlich, S. K., Reinke, A., Menze, B., … Piraud, M. (2023). Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps (arXiv:2312.02608). arXiv. https://doi.org/10.48550/arXiv.2312.02608
Kovacs, B., Netzer, N., Baumgartner, M., Schrader, A., Isensee, F., Weißer, C., Wolf, I., Görtz, M., Jaeger, P. F., Schütz, V., Floca, R., Gnirs, R., Stenzinger, A., Hohenfellner, M., Schlemmer, H.-P., Bonekamp, D., & Maier-Hein, K. H. (2023). Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer. Scientific Reports, 13(1), 19805. https://doi.org/10.1038/s41598-023-46747-z
Klein, L., Ziegler, S., Laufer, F., Debus, C., Götz, M., Maier‐Hein, K., Paetzold, U. W., Isensee, F., & Jäger, P. F. (2023). Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI. Advanced Materials, 2307160. https://doi.org/10.1002/adma.202307160
Abdallah, N., Wood, A., Benidir, T., Heller, N., Isensee, F., Tejpaul, R., Corrigan, D., Suk-Ouichai, C., Struyk, G., Moore, K., Venkatesh, N., Ergun, O., You, A., Campbell, R., Remer, E. M., Haywood, S., Krishnamurthi, V., Abouassaly, R., Campbell, S., … Weight, C. J. (2023). AI-generated R.E.N.A.L.+ Score Surpasses Human-generated Score in Predicting Renal Oncologic Outcomes. Urology, 180, 160–167. https://doi.org/10.1016/j.urology.2023.07.017
Kovacs, B., Netzer, N., Baumgartner, M., Eith, C., Bounias, D., Meinzer, C., Jaeger, P. F., Zhang, K. S., Floca, R., Schrader, A., Isensee, F., Gnirs, R., Goertz, M., Schuetz, V., Stenzinger, A., Hohenfellner, M., Schlemmer, H.-P., Wolf, I., Bonekamp, D., & Maier-Hein, K. H. (2023). Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection (arXiv:2309.03652). arXiv. https://doi.org/10.48550/arXiv.2309.03652
Gutsche, R., Lowis, C., Ziemons, K., Kocher, M., Ceccon, G., Brambilla, C. R., Shah, N. J., Langen, K.-J., Galldiks, N., Isensee, F., & Lohmann, P. (2023). Automated Brain Tumor Detection and Segmentation for Treatment Response Assessment Using Amino Acid PET. Journal of Nuclear Medicine: Official Publication, Society of Nuclear Medicine, jnumed.123.265725. https://doi.org/10.2967/jnumed.123.265725
Wald, T., Ulrich, C., Isensee, F., Zimmerer, D., Koehler, G., Baumgartner, M., & Maier-Hein, K. H. (2023). Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency (arXiv:2307.02516). arXiv. https://doi.org/10.48550/arXiv.2307.02516
Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Zhao, Z., Chen, H., Wang, L., Golts, A., Khapun, D., Shats, D., Shoshan, Y., Gilboa-Solomon, F., George, Y., Yang, X., Zhang, J., Zhang, J., Xia, Y., Wu, M., Liu, Z., … Weight, C. (2023). The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT (arXiv:2307.01984). arXiv. https://doi.org/10.48550/arXiv.2307.01984
Yang, L., Liu, Q., Kumar, P., Sengupta, A., Farnoud, A., Shen, R., Trofimova, D., Ziegler, S., Kutschke, D., Kreyling, W., Piraud, M., Isensee, F., Burgstaller, G., Rehberg, M., Stöger, T., & Schmid, O. (2023). 60 Multimodal Imaging and Artificial Intelligence Unveil Hot-Spot Deposition, Bronchial/Alveolar Dose and Cellular Fate of Biopersistent Nanoparticles in the Lung. Annals of Work Exposures and Health, 67, i53–i53. https://doi.org/10.1093/annweh/wxac087.129
Roy, S., Koehler, G., Baumgartner, M., Ulrich, C., Petersen, J., Isensee, F., & Maier-Hein, K. (2023). Transformer Utilization in Medical Image Segmentation Networks (arXiv:2304.04225). arXiv. https://doi.org/10.48550/arXiv.2304.04225
Eisenmann, M., Reinke, A., Weru, V., Tizabi, M. D., Isensee, F., Adler, T. J., Ali, S., Andrearczyk, V., Aubreville, M., Baid, U., Bakas, S., Balu, N., Bano, S., Bernal, J., Bodenstedt, S., Casella, A., Cheplygina, V., Daum, M., de Bruijne, M., … Maier-Hein, L. (2023). Why is the winner the best? (arXiv:2303.17719). arXiv. https://doi.org/10.48550/arXiv.2303.17719
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. (2023). Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study. Neuro-Oncology, 25(3), 533–543. https://doi.org/10.1093/neuonc/noac189
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
Wood, A., Benidir, T., Abdallah, N., Heller, N., Isensee, F., Tejpaul, R., Suk-ouichai, C., Curry, C., You, A., Remer, E. M., Haywood, S., Campbell, S., Papanikolopoulos, N., & Weight, C. J. (2023). Predictive accuracy of computer-generated C-index nephrometry scores compared with human-generated scores in predicting oncologic and perioperative outcomes. Journal of Clinical Oncology, 41(6_suppl), 623–623. https://doi.org/10.1200/JCO.2023.41.6_suppl.623
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., Lohöfer, F., Holch, J. W., Sommer, W., Hofmann, F., Hostettler, A., Lev-Cohain, N., Drozdzal, M., Amitai, M. M., Vivanti, R., … Menze, B. (2023). The Liver Tumor Segmentation Benchmark (LiTS). Medical Image Analysis, 84, 102680. https://doi.org/10.1016/j.media.2022.102680
Lüth, C. T., Zimmerer, D., Koehler, G., Jaeger, P. F., Isensee, F., Petersen, J., & Maier-Hein, K. H. (2023). CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization (arXiv:2301.02126). arXiv. https://doi.org/10.48550/arXiv.2301.02126
Maška, M., Ulman, V., Delgado-Rodriguez, P., Gómez-de-Mariscal, E., Nečasová, T., Guerrero Peña, F. A., Ren, T. I., Meyerowitz, E. M., Scherr, T., Löffler, K., Mikut, R., Guo, T., Wang, Y., Allebach, J. P., Bao, R., Al-Shakarji, N. M., Rahmon, G., Toubal, I. E., Palaniappan, K., … Ortiz-de-Solórzano, C. (2023). The Cell Tracking Challenge: 10 years of objective benchmarking. Nature Methods, 20(7), 1010–1020. https://doi.org/10.1038/s41592-023-01879-y
Roy, S., Koehler, G., Ulrich, C., Baumgartner, M., Petersen, J., Isensee, F., Jaeger, P. F., & Maier-Hein, K. (2023). MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation. https://doi.org/10.48550/ARXIV.2303.09975
Ulrich, C., Isensee, F., Wald, T., Zenk, M., Baumgartner, M., & Maier-Hein, K. H. (2023). MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation (Vol. 14222, pp. 648–658). https://doi.org/10.1007/978-3-031-43898-1_62
Isensee, F., Ulrich, C., Wald, T., & Maier-Hein, K. H. (2023). Extending nnU-Net Is All You Need. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 12–17). Springer Fachmedien. https://doi.org/10.1007/978-3-658-41657-7_7
Klein, L., Ziegler, S., Laufer, F., Debus, C., Götz, M., Maier-Hein, K., Paetzold, U., Isensee, F., & Jaeger, P. (2023). Understanding Scalable Perovskite Solar Cell Manufacturing with Explainable AI. https://publikationen.bibliothek.kit.edu/1000167169
Yang, L., Kumar, P., Sengupta, A., Farnoud, A., Shen, R., Trofimova, D., Ziegler, S., Davoudi, N., Doryab, A., Yildirim, A., Schiller, H., Razansky, D., Piraud, M., Liu, Q., Rehberg, M., Stoeger, T., Burgstaller, G., Kreyling, W., Isensee, F., & Schmid, O. (2023). Multimodal imaging and deep learning unveil pulmonary delivery profiles and acinar migration of tissue-resident macrophages in the lun. https://doi.org/10.21203/rs.3.rs-2994336/v1

Dr. rer. nat. Fabian Isensee
Head of Applied Computer Vision Lab Support Unit DKFZ

+49 6221 42 2353

f.isensee@dkfz-heidelberg.de

Helmholtz Imaging
German Cancer Research Center (DKFZ)
Im Neuenheimer Feld 280
D-69120 Heidelberg