Research Unit DKFZ
As digitalization leads to increasing volumes of data, advancing reliable AI-based image analysis is key to creating scientific and societal impact.
Image Analysis & Validation (DKFZ): we focus on the downstream stages of the imaging pipeline – specifically, the development, validation, and deployment of advanced AI-based methods for automated image analysis. These phases are critical for extracting high-level, domain-relevant information from complex imaging data. Our work addresses algorithmic challenges in interpreting, quantifying, and validating image-derived information, with the overarching goal of enabling trustworthy, robust, and generalizable AI-driven analysis.
We conduct interdisciplinary research in three core areas:
(1) automated image analysis, where we work to enhance the robustness and generalizability of AI methods across diverse and imperfect real-world datasets;
(2) human-machine interaction, which aims to integrate humans as active participants in AI development and deployment to ensure transparency, trust, and safety; and
(3) validation and benchmarking, where we lead efforts to develop and standardize validation practices and develop tools that enable reproducible and transparent assessments of algorithm performance.
By addressing these pivotal stages in the imaging pipeline, our mission is to advance the reliability and impact of AI-based image analysis across scientific and societal applications.
Publications
4725570
HI Science Unit DKFZ
1
https://helmholtz-imaging.de/apa-bold-title.csl
50
date
147
https://helmholtz-imaging.de/wp-content/plugins/zotpress/
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Imran, M., Krebs, J. R., Sivaraman, V. B., Zhang, T., Kumar, A., Ueland, W. R., Fassler, M. J., Huang, J., Sun, X., Wang, L., Shi, P., Rokuss, M., Baumgartner, M., Kirchhof, Y., Maier-Hein, K. H., Isensee, F., Liu, S., Han, B., Nguyen, B. T., … Shao, W. (2025). Multi-class segmentation of aortic branches and zones in computed tomography angiography: The aortaseg24 challenge. arXiv Preprint arXiv:2502.05330. https://arxiv.org/abs/2502.05330
Zenk, M., Zimmerer, D., Isensee, F., Traub, J., Norajitra, T., Jäger, P. F., & Maier-Hein, K. (2025). Comparative benchmarking of failure detection methods in medical image segmentation: unveiling the role of confidence aggregation. Medical Image Analysis. https://www.sciencedirect.com/science/article/pii/S1361841524003177
Zenk, M., Baid, U., Pati, S., Linardos, A., Edwards, B., Sheller, M., Foley, P., Aristizabal, A., Zimmerer, D., Gruzdev, A., Martin, J., Shinohara, R. T., Reinke, A., Isensee, F., Parampottupadam, S., Parekh, K., Floca, R., Kassem, H., Baheti, B., … Mongan, J. (2025). Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge. Nature Communications. https://www.nature.com/articles/s41467-025-60466-1
Zalevskyi, V., Sanchez, T., Kaandorp, M., Roulet, M., Fajardo-Rojas, D., Li, L., Hutter, J., Li, H. B., Barkovich, M., Ji, H., Wilhelmi, L., Dändliker, A., Steger, C., Koob, M., Gomez, Y., Jakovčić, A., Klaić, M., Adžić, A., Marković, P., … Cuadra, M. B. (2025). Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge. arXiv Preprint arXiv:2505.02784. https://arxiv.org/abs/2505.02784
Wennmann, M., Kächele, J., Salomon, A. von, Nonnenmacher, T., Bujotzek, M., Xiao, S., Mora, A. M., Hielscher, T., Hajiyianni, M., Menis, E., Grözinger, M., Bauer, F., Riebl, V., Rotkopf, L. T., Zhang, K. S., Afat, S., Besemer, B., Hoffmann, M., Ringelstein, A., … Maier-Hein, K. (2025). Automated Detection of Focal Bone Marrow Lesions From MRI: A Multi-center Feasibility Study in Patients with Monoclonal Plasma Cell Disorders. Academic Radiology. https://www.sciencedirect.com/science/article/pii/S1076633225006142
Yang, S., Zhou, F., Mayer, L., Huang, F., Chen, Y., Wang, Y., He, S., Nie, Y., Wang, X., Sümer, Ö., Jin, Y., Sun, H., Xu, S., Liu, A. Q., Li, Z., Qin, J., Teoh, J. Y., Maier-Hein, L., & Chen, H. (2025). Large-scale Self-supervised Video Foundation Model for Intelligent Surgery. arXiv Preprint arXiv:2506.02692. https://arxiv.org/abs/2506.02692
Wald, T., Hamm, B., Holzschuh, J. C., Shafie, R. E., Kudak, A., Kovacs, B., Pflüger, I., Nettelbladt, B. von, Ulrich, C., Baumgartner, M. A., Vollmuth, P., Debus, J., Maier-Hein, K. H., & Welzel, T. (2025). Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI. European Radiology Experimental. https://link.springer.com/article/10.1186/s41747-025-00554-5
Twilt, J. J., Saha, A., Bosma, J. S., Padhani, A. R., Bonekamp, D., Giannarini, G., Bergh, R. van den, Kasivisvanathan, V., Obuchowski, N., Yakar, D., Elschot, M., Veltman, J., Fütterer, J., Huisman, H., Rooij, M. de, Fütterer, J. J., Bjartell, A., Villeirs, G., Salomon, G., … Consortium, P.-C. (2025). AI-assisted vs unassisted identification of prostate cancer in magnetic resonance images. JAMA Network Open. https://jamanetwork.com/journals/jamanetworkopen/article-abstract/2835296
Seidlitz, S., Hölzl, K., Garrel, A. von, Sellner, J., Katzenschlager, S., Hölle, T., Fischer, D., Forst, M. von der, Schmitt, F. C., Studier-Fischer, A., Weigand, M. A., Maier-Hein, L., & Dietrich, M. (2025). AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients. Science Advances. https://www.science.org/doi/abs/10.1126/sciadv.adw1968
Santos, E., Lopez-Navarro, J. M., Suarez-Gutierrez, M. A., Holzwarth, N., Albiña-Palmarola, P., Kirchner, T., Hernandez-Aguilera, A., Fernandez-Amador, J. A., Vazifehdan, F., Woitzik, J., Maier-Hein, L., & Sanchez-Porras, R. (2025). Depth-specific hypoxic responses to spreading depolarizations in gyrencephalic swine cortex unveiled by photoacoustic imaging. Translational Stroke Research. https://link.springer.com/article/10.1007/s12975-024-01247-8
Sang, Y., Liu, Y., Yibulayimu, S., Wang, Y., Killeen, B. D., Liu, M., Ku, P.-C., Johannsen, O., Gotkowski, K., Zenk, M., Maier-Hein, K., Isensee, F., Yue, P., Wang, Y., Yu, H., Pan, Z., He, Y., Liang, X., Liu, D., … Wang, Y. (2025). Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge. arXiv Preprint arXiv:2504.02382. https://arxiv.org/abs/2504.02382
Salomon, A. von, Kächele, J., Nonnenmacher, T., Bujotzek, M., Xiao, S., Mora, A. M., Hajiyianni, M., Menis, E., Grözinger, M., Bauer, F., Riebl, V., Hielscher, T., Besemer, B., Afat, S., Graeven, U., Ringelstein, A., Hänel, M., Fedders, D., Ljimani, A., … Wennmann, M. (2025). Automatische Detektion von fokalen Läsionen im MRT bei Patienten mit Multiplem Myelom–eine multizentrische Machbarkeitsstudie. RöFo-Fortschritte Auf Dem Gebiet Der Röntgenstrahlen Und Der Bildgebenden Verfahren. https://www.thieme-connect.com/products/ejournals/html/10.1055/s-0045-1802830
Rokuss, M., Kirchhoff, Y., Akbal, S., Kovacs, B., Roy, S., Ulrich, C., Wald, T., Rotkopf, L. T., Schlemmer, H.-P., & Maier-Hein, K. (2025). LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging. https://openaccess.thecvf.com/content/CVPR2025/html/Rokuss_LesionLocator_Zero-Shot_Universal_Tumor_Segmentation_and_Tracking_in_3D_Whole-Body_CVPR_2025_paper.html
Rueckert, T., Rauber, D., Maerkl, R., Klausmann, L., Yildiran, S. R., Gutbrod, M., Nunes, D. W., Moreno, A. F., Luengo, I., Stoyanov, D., Toussaint, N., Cho, E., Kim, H. B., Choo, O. S., Kim, K. Y., Kim, S. T., Arantes, G., Song, K., Zhu, J., … Palm, C. (2025). Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge. arXiv Preprint arXiv:2507.16559. https://arxiv.org/abs/2507.16559
Rokuss, M., Hamm, B., Kirchhoff, Y., & Maier-Hein, K. (2025). Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation. arXiv Preprint arXiv:2507.13830. https://arxiv.org/abs/2507.13830
Reinke, A. (2025). Validierung von künstliche Intelligenz-Algorithmen für die chirurgische Praxis. https://link.springer.com/article/10.1007/s00104-025-02348-2
Peretzke, R., Neher, P. F., Brandt, G. A., Fritze, S., Volkmer, S., Daub, J., Northoff, G., Bohn, J., Kirchhoff, Y., Roy, S., Maier-Hein, K. H., Meyer-Lindenberg, A., & Hirjak, D. (2025). Deciphering white matter microstructural alterations in catatonia according to ICD-11: replication and machine learning analysis. Molecular Psychiatry. https://www.nature.com/articles/s41380-024-02821-0
Pei, L., Sutton, G., Rutherford, M., Wagner, U., Nolan, T., Smith, K., Farmer, P., Gu, P., Rana, A., Chen, K., Ferleman, T., Park, B., Wu, Y., Kojouharov, J., Singh, G., Lemon, J., Willis, T., Vukadinovic, M., Duffy, G., … Farahani, K. (2025). Medical Image De-Identification Benchmark Challenge. arXiv Preprint arXiv:2507.23608. https://arxiv.org/abs/2507.23608
Parampottupadam, S., Coşğun, M., Pati, S., Zenk, M., Roy, S., Bounias, D., Hamm, B., Sav, S., Floca, R., & Maier-Hein, K. (2025). Inclusive, Differentially Private Federated Learning for Clinical Data. arXiv Preprint arXiv:2505.22108. https://arxiv.org/abs/2505.22108
Palm, C., Breininger, K., Deserno, T., Handels, H., Maier, A., Maier-Hein, K. H., & Tolxdorff, T. M. (2025). Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025. https://books.google.com/books?hl=en&lr=&id=RkxLEQAAQBAJ&oi=fnd&pg=PR5&dq=info:ntotaTK1rHoJ:scholar.google.com&ots=6y_ZvxGKJ2&sig=lcCcH6i8Y4kvOLRgtnxC5yMIksk
Ertl, A., Xiao, S., Denner, S., Peretzke, R., Zimmerer, D., Neher, P., Isensee, F., & Maier-Hein, K. (2025). nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection. arXiv Preprint arXiv:2504.06742. https://arxiv.org/abs/2504.06742
Denner, S., Zimmerer, D., Bounias, D., Bujotzek, M., Xiao, S., Stock, R., Kausch, L., Schader, P., Penzkofer, T., Jäger, P. F., & Maier-Hein, K. (2025). Leveraging foundation models for content-based image retrieval in radiology. Computers in Biology and Medicine. https://www.sciencedirect.com/science/article/pii/S0010482525009916
Denner, S., Kovacs, B., Zimmerer, D., Krishnaswamy, D., Bounias, D., Stock, R., Bujotzek, M. R., Imrie, F., Fedorov, A., & Maier-Hein, K. (2025). Fine-tuning Vision Foundation Models for Multi-Modal Prostate MR Sequence Classification. https://openreview.net/forum?id=yLplH8pVmH
Palm, V., Thangamani, S., Budai, B. K., Skornitzke, S., Eckl, K., Tong, E., Sedaghat, S., Heußel, C. P., Stackelberg, O. von, Engelhardt, S., Kopytova, T., Norajitra, T., Maier-Hein, K. H., Kauczor, H.-U., & Wielpütz, M. O. (2025). AI-based CT assessment of 3117 vertebrae reveals significant sex-specific vertebral height differences. Scientific Reports. https://www.nature.com/articles/s41598-025-05091-0
Nohel, M., Ulrich, C., Suprijadi1, J., & Wald, T. (2025). Unified Framework for Foreground and Anonymization. Bildverarbeitung Für Die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025. https://books.google.com/books?hl=en&lr=&id=RkxLEQAAQBAJ&oi=fnd&pg=PA242&dq=info:1tIB915QrjAJ:scholar.google.com&ots=6y_ZvxGNQW&sig=npxV4MGsGvhwVYRy6nnWoBAx7hQ
Nickel, F., Studier-Fischer, A., Özdemir, B., Odenthal, J., Müller, L. R., Knoedler, S., Kowalewski, K. F., Camplisson, I., Allers, M. M., Dietrich, M., Schmidt, K., Salg, G. A., Kenngott, H. G., Billeter, A. T., Gockel, I., Sagiv, C., Hadar, O. E., Gildenblat, J., Ayala, L., … Müller-Stich, B. P. (2025). Optimization of anastomotic technique and gastric conduit perfusion with hyperspectral imaging and machine learning in an experimental model for minimally invasive esophagectomy. European Journal of Surgical Oncology. https://www.sciencedirect.com/science/article/pii/S0748798323004444
Navarro-Garcia, D., Marcos, A., Beets-Tan, R., Blomqvist, L., Bodalal, Z., Deandreis, D., Crispin-Ortuzar, M., Gallagher, F., Giandini, T., Graves, M. J., Lassau, N., Maier-Hein, K., Prelaj, A., Schader, P., Schlemmer, H.-P., Sedlaczek, O., Vaiani, M., & Perez-Lopez, R. (2025). Real-world radiology data for artificial intelligence-driven cancer support systems and biomarker development. https://www.sciencedirect.com/science/article/pii/S2949820125000098
Moons, K. G., Damen, J. A., Kaul, T., Hooft, L., Navarro, C. A., Dhiman, P., Beam, A. L., Calster, B. V., Celi, L. A., Denaxas, S., Denniston, A. K., Ghassemi, M., Heinze, G., Kengne, A. P., Maier-Hein, L., Liu, X., Logullo, P., McCradden, M. D., Liu, N., … Smeden, M. van. (2025). PROBAST+ AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. Bmj. https://www.bmj.com/content/388/bmj-2024-082505.abstract
Luo, X., Fu, J., Zhong, Y., Liu, S., Han, B., Astaraki, M., Bendazzoli, S., Toma-Dasu, I., Ye, Y., Chen, Z., Xia, Y., Su, Y., Ye, J., He, J., Xing, Z., Wang, H., Zhu, L., Yang, K., Fang, X., … Zhang, S. (2025). Segrap2023: A benchmark of organs-at-risk and gross tumor volume segmentation for radiotherapy planning of nasopharyngeal carcinoma. Medical Image Analysis. https://www.sciencedirect.com/science/article/pii/S1361841524003748
Maier-Hein, L., Wirkert, S. J., VEMURI, A. S., Menjivar, L. A. A., Seidlitz, S., Kirchner, T., & Adler, T. (2025). Method and system for augmented imaging in open treatment using multispectral information. https://patents.google.com/patent/US12329491B2/en
Luo, G., Xu, M., Chen, H., Liang, X., Tao, X., Ni, D., Jeong, H., Kim, C., Stock, R., Baumgartner, M., Kirchhoff, Y., Rokuss, M., Maier-Hein, K., Yang, Z., Fan, T., Boutry, N., Tereshchenko, D., Moine, A., Charmetant, M., … Dong, S. (2025). Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge. arXiv Preprint arXiv:2501.15588. https://arxiv.org/abs/2501.15588
Knopp, M., Bender, C. J., Holzwarth, N., Li, Y., Kempf, J., Caranovic, M., Knieling, F., Lang, W., Rother, U., Seitel, A., Maier-Hein, L., & Dreher, K. K. (2025). Shortcut learning leads to sex bias in deep learning models for photoacoustic tomography. International Journal of Computer Assisted Radiology and Surgery. https://link.springer.com/article/10.1007/s11548-025-03370-9
Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., Kaissis, G., Tsakou, G., Buvat, I., Kalpathy-Cramer, J., Mongan, J., Schnabel, J. A., Kushibar, K., Riklund, K., Marias, K., … Starmans, M. P. (2025). FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. Bmj. https://www.bmj.com/content/388/bmj-2024-081554.abstract
Kirchhoff, Y., Rokuss, M., Hamm, B., Ravindran1, A., Ulrich, C., & Maier-Hein, K. (2025). Efficient Cross-Modality Abdominal Organ Segmentation Using nnU-Net. Fast, Low-Resource, Accurate Robust Organ and Pan-Cancer Segmentation: MICCAI Challenge, FLARE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings. https://books.google.com/books?hl=en&lr=&id=oBxtEQAAQBAJ&oi=fnd&pg=PA243&dq=info:Kf-e6dKY9X0J:scholar.google.com&ots=rsmpN57Z9X&sig=ttlMbE5qVmIcMO7ZYncNXQ00GRY
Other Research
Projects
Helmholtz Imaging Projects are granted to cross-disciplinary research teams that identify innovative research topics at the intersection of imaging and information & data science, initiate cross-cutting research collaborations, and thus underpin the growth of the Helmholtz Imaging network. These annual calls are based on the general concept for Helmholtz Imaging and are in line with the future topics of the Initiative and Networking Fund (INF).
Research Unit DESY
The Research Unit at DESY focuses on the early stages of the imaging pipeline, developing methods for advanced image reconstruction, including the optimization of measurements and the combination of classical methods with data-driven approaches.
Our goal is to drag out a maximal amount of (quantitative) information from given or designed measurements.
Research Unit MDC
The Research Unit at MDC focuses on integrating heterogeneous imaging data across modalities, scales, and time. We develop concepts and algorithms for generic processing, stitching, fusion, and visualization of large, high-dimensional datasets.
Our aim is to enable seamless analysis of complex imaging data without restrictions on the underlying modalities.
Publications
Helmholtz Imaging captures the world of science. Discover unique data sets, ready-to-use software tools, and top-level research papers. The platform’s output originates from our research groups as well as from projects funded by us, theses supervised by us and collaborations initiated through us. Altogether, this showcases the whole diversity of Helmholtz Imaging.