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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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
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
Jiménez-Sánchez, A., Avlona, N.-R., Boer, S. de, Campello, V. M., Feragen, A., Ferrante, E., Ganz, M., Gichoya, J. W., Gonzalez, C., Groefsema, S., Hering, A., Hulman, A., Joskowicz, L., Juodelyte, D., Kandemir, M., Kooi, T., Lérida, J. del P., Li, L. Y., Pacheco, A., … Cheplygina, V. (2025). In the picture: Medical imaging datasets, artifacts, and their living review. https://dl.acm.org/doi/abs/10.1145/3715275.3732035
Egen, L., Hommel, M., Haney, C. M., Özdemir, B., Knoedler, S., Sellner, J., Seidlitz, S., Dietrich, M., Salg, G. A., Nickel, F., Maier-Hein, L., Michel, M. S., Studier-Fischer, A., & Kowalewski, K.-F. (2025). Hyperspectral Imaging Accurately Detects Renal Malperfusion Due to High Intrarenal Pressure. European Urology Open Science. https://www.sciencedirect.com/science/article/pii/S2666168325002411
Antonelli, M., Farahani, K., Sudre, C. H., Cardoso, M. J., Maier-Hein, L., & Reinke, A. (2025). Challenges as a framework for trustworthy AI. https://www.sciencedirect.com/science/article/pii/B9780443237614000201
Aumente-Maestro, C., González, D. R., Martinez, D., & Remeseiro, B. (2025). BTS U-Net: A Data-Driven Approach to Brain Tumor Segmentation Through Deep Learning. https://www.sciencedirect.com/science/article/pii/S1746809425000011
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
Carstens, M., Vasisht, S., Zhang, Z., Barbur, I., Reinke, A., Maier-Hein, L., Hashimoto, D. A., & Kolbinger, F. R. (2025). Artificial Intelligence for Surgical Scene Understanding: A Systematic Review and Reporting Quality Meta-Analysis. https://www.medrxiv.org/content/10.1101/2025.07.12.25330122.abstract
Dreher, K. K., Gröhl, J., Grace, F., Ayala, L., Nölke, J.-H., Bender, C. J., Watt, M. J., White, K.-L., Tao, R., Johnen, W., Tizabi, M. D., Seitel, A., Maier-Hein, L., & Bohndiek, S. E. (2025). Anthropomorphic tissue-mimicking phantoms for oximetry validation in multispectral optical imaging. Journal of Biomedical Optics. https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-30/issue-7/076006/Anthropomorphic-tissue-mimicking-phantoms-for-oximetry-validation-in-multispectral-optical/10.1117/1.JBO.30.7.076006.short
Zhang, K. S., Neelsen, C. J. O., Wennmann, M., Hielscher, T., Kovacs, B., Glemser, P. A., Görtz, M., Stenzinger, A., Maier-Hein, K. H., Huber, J., Schlemmer, H.-P., & Bonekamp, D. (2025). In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning. Scientific Reports. https://www.nature.com/articles/s41598-025-09989-7
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, Wittmann, B., Waldmannstetter, D., Kofler, F., Navarro, F., Menten, M. J., … Cui, Y. (2025). Benchmarking the cow with the topcow challenge: Topology-aware anatomical segmentation of the circle of willis for cta and mra. ArXiv. https://pmc.ncbi.nlm.nih.gov/articles/PMC10793481/
Yamlahi, A., Kalinowski, P., Godau, P., Younis, R., Wagner, M., Müller, B., & Maier-Hein, L. (2025). Smarter Self-distillation: Optimizing the Teacher for Surgical Video Applications. https://link.springer.com/chapter/10.1007/978-3-032-05114-1_50
Simons, L., Alasfar, L., Qadoura, M., Buhl, J., Sunderer, F., Korell, F., Ikonomidis, I., Dietrich, M., Seidlitz, S., Vink, H., Maier-Hein, L., Schmitt, M., Schlenk, R. F., Müller-Tidow, C., Dreger, P., & Luft, T. (2025). Comprehensive assessment of endothelial dysfunction before cellular therapy: EASIX, local imaging and systemic biomarkers. Blood Vessels, Thrombosis & Hemostasis. https://ashpublications.org/bloodvth/article/doi/10.1016/j.bvth.2025.100105/547211
Shein, G. S., Bannone, E., Seidlitz, S., Hassouna, M., Baratelli, L., Pardo, A., Lecler, S., Triponez, F., Chand, M., Gioux, S., Maier-Hein, L., & Diana, M. (2025). Surgical optomics: a new science towards surgical precision. https://www.nature.com/articles/s44355-025-00035-x
Schulze, A., Haselbeck-Köbler, M., Brandenburg, J. M., Daum, M. T. J., März, K., Hornburg, S., Maurer, H., Myers, F., Reichert, G., Bodenstedt, S., Nickel, F., Kriegsmann, M., Wielpütz, M. O., Speidel, S., Maier-Hein, L., Müller-Stich, B. P., Mehrabi, A., & Wagner, M. (2025). Aliado-A design concept of AI for decision support in oncological liver surgery. European Journal of Surgical Oncology. https://www.sciencedirect.com/science/article/pii/S0748798324007212
Rokuss, M., Kirchhoff, Y., Isensee, F., & Maier-Hein, K. H. (2025). Towards Interactive Lesion Segmentation in Whole-Body PET/CT with Promptable Models. arXiv Preprint arXiv:2508.21680. https://arxiv.org/abs/2508.21680
Reinke, A. (2025). Validation of artificial intelligence algorithms for the surgical practice.[Validierung von künstliche Intelligenz-Algorithmen für die chirurgische Praxis.]. https://inrepo02.dkfz.de/record/303010
Reinke, A., Tizabi, M. D., & Maier‐Hein, L. (2025). Advancing standards in biomedical image analysis validation: A perspective on Metrics Reloaded. Clinical and Translational Medicine. https://pmc.ncbi.nlm.nih.gov/articles/PMC12399785/
Mühlbauer, J., Gottstein, L., Egen, L., Haney, C., Studier‐Fischer, A., Christodoulou, E., Cacciamani, G. E., März, K., Maier‐Hein, L., Michel, S. M., Quan, A., & Kowalewski, K.-F. (2025). AI‐driven preoperative risk assessment in kidney cancer surgery: A comparative feasibility study of machine learning models. BJUI Compass. https://bjui-journals.onlinelibrary.wiley.com/doi/abs/10.1002/bco2.70080
Lüth, C. T., Traub, J., Kahl, K.-C., Bungert, T., Klein, L., Kraemer, L., Jaeger, P. F., Isensee, F., & Maier-Hein, K. (2025). nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation. Transactions on Machine Learning Research (TMLR).
Kirchhoff, Y., Rokuss, M., Isensee, F., & Maier-Hein, K. H. (2025). Promptable Longitudinal Lesion Segmentation in Whole-Body CT. arXiv Preprint arXiv:2509.00613. https://arxiv.org/abs/2509.00613
Häntze, H., Xu, L., Mertens, C. J., Dorfner, F. J., Donle, L., Busch, F., Kader, A., Ziegelmayer, S., Bayerl, N., Navab, N., Rueckert, D., Schnabel, J., Aerts, H. J., Truhn, D., Bamberg, F., Weiss, J., Schlett, C. L., Ringhof, S., Niendorf, T., … Bressem, K. K. (2025). Segmenting whole-body MRI and CT for multiorgan anatomic structure delineation. Radiology: Artificial Intelligence. https://pubs.rsna.org/doi/abs/10.1148/ryai.240777
Haghiri, H., Baidya, R., Dvoretskii, S., Maier-Hein, K. H., & Nolden, M. (2025). A Hybrid AI-based and Rule-based Approach to DICOM De-identification: A Solution for the MIDI-B Challenge. arXiv Preprint arXiv:2509.00437. https://arxiv.org/abs/2509.00437
Full, P. M., Schirrmeister, R. T., Hein, M., Russe, M. F., Reisert, M., Ammann, C., Greiser, K. H., Niendorf, T., Pischon, T., Schulz-Menger, J., Maier-Hein, K. H., Bamberg, F., Rospleszcz, S., Schlett, C. L., & Schuppert, C. (2025). Cardiac Magnetic Resonance Imaging in the German National Cohort (NAKO): Automated Segmentation of Short-Axis Cine Images and Post-Processing Quality Control. Journal of Cardiovascular Magnetic Resonance. https://www.sciencedirect.com/science/article/pii/S1097664725001206
Eckstein, K., Ulrich, C., Baumgartner, M., Kächele, J., Bounias, D., Wald, T., Floca, R., & Maier-Hein, K. H. (2025). The Missing Piece: A Case for Pre-training in 3D Medical Object Detection. https://link.springer.com/chapter/10.1007/978-3-032-04965-0_58
Disch, N. A., Kirchhoff, Y., Peretzke, R., Rokuss, M., Roy, S., Ulrich, C., Zimmerer, D., & Maier-Hein, K. (2025). Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging. arXiv Preprint arXiv:2508.21580. https://arxiv.org/abs/2508.21580
Durugol, O. F., Rokuss, M., Kirchhoff, Y., & Maier-Hein, K. H. (2025). A Multi-Stage Fine-Tuning and Ensembling Strategy for Pancreatic Tumor Segmentation in Diagnostic and Therapeutic MRI. arXiv Preprint arXiv:2508.21775. https://arxiv.org/abs/2508.21775
Bounias, D., Simons, L., Baumgartner, M., Ehring, C., Neher, P., Kapsner, L. A., Kovacs, B., Floca, R., Jaeger, P. F., Eberle, J., Hadler, D., Laun, F. B., Ohlmeyer, S., Maier-Hein, L., Uder, M., Wenkel, E., Maier-Hein, K. H., & Bickelhaupt, S. (2025). Including AI in diffusion-weighted breast MRI has potential to increase reader confidence and reduce workload. Journal of the American Medical Informatics Association. https://academic.oup.com/jamia/advance-article-abstract/doi/10.1093/jamia/ocaf156/8262149
Collins, G. S., Moons, K. G., Dhiman, P., Riley, R. D., Beam, A. L., Calster, B. V., Ghassemi, M., Liu, X., Reitsma, J. B., Smeden, M. van, Boulesteix, A.-L., Camaradou, J. C., Celi, L. A., Denaxas, S., Denniston, A. K., Glocker, B., Golub, R. M., Harvey, H., Heinze, G., … Logullo, P. (2025). TRIPOD+ AI 지침: 회귀 또는 머신러닝 방법을 사용하는 임상 예측모델 보고를 위한 최신 지침. Ewha Medical Journal. https://pmc.ncbi.nlm.nih.gov/articles/PMC12365671/
Baumgartner, M. A. (2025). Generalised Medical Object Detection via Self-Configuring Method Design. https://archiv.ub.uni-heidelberg.de/volltextserver/37068/
André1, P., Heitz1, C., & Christodoulou, E. (2025). Some hidden traps of confidence intervals in medical image segmentation: coverage issues. Bridging Regulatory Science and Medical Imaging Evaluation; and Distributed, Collaborative, and Federated Learning: First International Workshop, BRIDGE 2025, and 6th International Workshop, DeCaF 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23 and September 27, 2025, Proceedings. https://books.google.com/books?hl=en&lr=&id=AQWJEQAAQBAJ&oi=fnd&pg=PA15&dq=info:s3tB3H18ALAJ:scholar.google.com&ots=twutYW14-Z&sig=a7jnQW0et8vMR9UETYhTZZbjhn0
Aumente-Maestro, C., Díez, J., & Remeseiro, B. (2025). A multi-task framework for breast cancer segmentation and classification in ultrasound imaging. https://www.sciencedirect.com/science/article/pii/S0169260724005339
Wald, T., Roy, S., Isensee, F., Ulrich, C., Ziegler, S., Trofimova, D., Stock, R., Baumgartner, M., Köhler, G., & Maier-Hein, K. (2025). Primus: Enforcing attention usage for 3d medical image segmentation. arXiv Preprint arXiv:2503.01835. https://arxiv.org/abs/2503.01835
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 Preprint arXiv:2503.08373. https://arxiv.org/abs/2503.08373
Ulrich, C., Wald, T., Isensee, F., & Maier-Hein, K. H. (2025). Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation. arXiv Preprint arXiv:2504.06741. https://arxiv.org/abs/2504.06741
Mayer, L., Rädsch, T., Michael, D., Luttner, L., Yamlahi, A., Christodoulou, E., Godau, P., Knopp, M., Reinke, A., Kolbinger, F., & Maier-Hein, L. (2025). Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study. arXiv Preprint arXiv:2506.06232. https://arxiv.org/abs/2506.06232
Rädsch, T., Mayer, L., Pavicic, S., Kavur, A. E., Knopp, M., Öztürk, B., Maier-Hein, K., Jaeger, P. F., Isensee, F., Reinke, A., & Maier-Hein, L. (2025). Bridging vision language model (VLM) evaluation gaps with a framework for scalable and cost-effective benchmark generation. arXiv Preprint arXiv:2502.15563. https://arxiv.org/abs/2502.15563
Wald, T., Ulrich, C., Lukyanenko, S., Goncharov, A., Paderno, A., Miller, M., Maerkisch, L., Jaeger, P., & Maier-Hein, K. (2025). Revisiting MAE pre-training for 3D medical image segmentation. https://openaccess.thecvf.com/content/CVPR2025/html/Wald_Revisiting_MAE_Pre-training_for_3D_Medical_Image_Segmentation_CVPR_2025_paper.html
Fischer, M., Neher, P., Schüffler, P., Ziegler, S., Xiao, S., Peretzke, R., Clunie, D., Ulrich, C., Baumgartner, M., Muckenhuber, A., Almeida, S. D., Gőtz, M., Kleesiek, J., Nolden, M., Braren, R., & Maier-Hein, K. (2025). Unlocking the potential of digital pathology: Novel baselines for compression. Journal of Pathology Informatics. https://www.sciencedirect.com/science/article/pii/S2153353925000033
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.