Our publications show the whole diversity of Helmholtz Imaging. They include software solutions and data sets as well as classical work. Software solutions can be downloaded here.

They originate from our research groups as well as from projects funded by us, theses supervised by us and collaborations initiated through us.

To be listed here, Helmholtz Imaging must have made a significant contribution to the provision of the software, be mentioned in the acknowledgements, or provide at least one of the authors.

Your contribution is missing? Write to us:

The purpose of this publication archive is not only to provide bibliographic data of Helmholtz Imaging publications, but also to provide access to the full text, as far as this is possible with respect to copyright.



Afifi, A. J., Thiele, S. T., Lorenz, S., Ghamisi, P., Tolosana-Delgado, R., Kirsch, M., Gloaguen, R., & Heizmann, M. (2023). Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciences (arXiv:2305.09928). arXiv.
Almeida, S. D., Lüth, C. T., Norajitra, T., Wald, T., Nolden, M., Jaeger, P. F., Heussel, C. P., Biederer, J., Weinheimer, O., & Maier-Hein, K. (2023). cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations (arXiv:2307.07254). arXiv.
Arteaga Cardona, F., Jain, N., Popescu, R., Busko, D., Madirov, E., Arús, B. A., Gerthsen, D., De Backer, A., Bals, S., Bruns, O. T., Chmyrov, A., Van Aert, S., Richards, B. S., & Hudry, D. (2023). Preventing cation intermixing enables 50% quantum yield in sub-15 nm short-wave infrared-emitting rare-earth based core-shell nanocrystals. Nature Communications, 14(1), 4462.
Bounias, D., Baumgartner, M., Neher, P., Kovacs, B., Floca, R., Jaeger, P. F., Kapsner, L., Eberle, J., Hadler, D., Laun, F., Ohlmeyer, S., Maier-Hein, K., & Bickelhaupt, S. (2023, July 19). Risk-adjusted Training and Evaluation for Medical Object Detection in Breast Cancer MRI. ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH).
Brugnara, G., Baumgartner, M., Scholze, E. D., Deike-Hofmann, K., Kades, K., Scherer, J., Denner, S., Meredig, H., Rastogi, A., Mahmutoglu, M. A., Ulfert, C., Neuberger, U., Schönenberger, S., Schlamp, K., Bendella, Z., Pinetz, T., Schmeel, C., Wick, W., Ringleb, P. A., … Vollmuth, P. (2023). Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke. Nature Communications, 14(1), 4938.
Bungert, T. J., Kobelke, L., & Jaeger, P. F. (2023). Understanding Silent Failures in Medical Image Classification.
Burger, M., & Esposito, A. (2023). Porous medium equation and cross-diffusion systems as limit of nonlocal interaction. Nonlinear Analysis, 235, 113347.
Burger, M., & Schulz, S. (2023). Well-posedness and stationary states for a crowded active Brownian system with size-exclusion (arXiv:2309.17326). arXiv.
Burger, M., Schuster, T., & Wald, A. (2023). Ill-posedness of time-dependent inverse problems in Lebesgue-Bochner spaces (arXiv:2310.08600). arXiv.
Burger, M., Kanzler, L., & Wolfram, M.-T. (2023). Boltzmann mean-field game model for knowledge growth: limits to learning and general utilities (arXiv:2209.04677). arXiv.
Calatroni, L., Donatelli, M., Morigi, S., Prato, M., & Santacesaria, M. (2023). Scale Space and Variational Methods in Computer Vision: 9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21–25, 2023, Proceedings. Springer Nature. DOI: 10.1007/978-3-031-31975-4_52
Ehrhardt, M. J., Kuger, L., & Schönlieb, C.-B. (2023). Proximal Langevin Sampling With Inexact Proximal Mapping (arXiv:2306.17737). arXiv.
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.
Fazeny, A., Tenbrinck, D., & Burger, M. (2023). Hypergraph p-Laplacians, Scale Spaces, and Information Flow in Networks. In L. Calatroni, M. Donatelli, S. Morigi, M. Prato, & M. Santacesaria (Eds.), Scale Space and Variational Methods in Computer Vision (pp. 677–690). Springer International Publishing.
Genthe, E., Miletic, S., Tekkali, I., Hennell James, R., Marlovits, T. C., & Heuser, P. (2023). PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms. Journal of Structural Biology, 215(3), 107990.
Godau, P., Kalinowski, P., Christodoulou, E., Reinke, A., Tizabi, M., Ferrer, L., Jäger, P., & Maier-Hein, L. (2023). Deployment of Image Analysis Algorithms under Prevalence Shifts.
Graf, O., Krahmer, F., & Krause-Solberg, S. (2023). One-bit Sigma-Delta modulation on the circle. Advances in Computational Mathematics, 49(3), 32.
Granero, A. C. (2023). UTILE-Oxy - Deep Learning to Automate Video Analysis of Bubble Dynamics in Proton Exchange Membrane Electrolyzers. (Original work published 2023)
Grote, L., Hussak, S.-A., Albers, L., Stachnik, K., Mancini, F., Seyrich, M., Vasylieva, O., Brückner, D., Lyubomirskiy, M., Schroer, C. G., & Koziej, D. (2023). Multimodal imaging of cubic Cu2O@Au nanocage formation via galvanic replacement using X-ray ptychography and nano diffraction. Scientific Reports, 13(1), 318.
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.
Hamdan, S., More, S., Sasse, L., Komeyer, V., Patil, K. R., & Raimondo, F. (2023). Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models (arXiv:2310.12568). arXiv.
Kabri, S., Roith, T., Tenbrinck, D., & Burger, M. (2023). Resolution-Invariant Image Classification Based on Fourier Neural Operators. In L. Calatroni, M. Donatelli, S. Morigi, M. Prato, & M. Santacesaria (Eds.), Scale Space and Variational Methods in Computer Vision (pp. 236–249). Springer International Publishing.
Kasahara, K., Leygeber, M., Seiffarth, J., Ruzaeva, K., Drepper, T., Nöh, K., & Kohlheyer, D. (2023). Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis. Frontiers in Microbiology, 14.
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.
Lingg, J. G. P., Bischof, T. S., Arús, B. A., Cosco, E. D., Sletten, E. M., Rowlands, C. J., Bruns, O. T., & Chmyrov, A. (2023). Shortwave-Infrared Line-Scan Confocal Microscope for Deep Tissue Imaging in Intact Organs. Laser & Photonics Reviews, 17(11), 2300292.
Lüth, C. T., Zimmerer, D., Koehler, G., Jaeger, P. F., Isenensee, F., & Maier-Hein, K. H. (2023). Contrastive Representations for Unsupervised Anomaly Detection and Localization. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 246–252). Springer Fachmedien Wiesbaden.
Malin-Mayor, C., Hirsch, P., Guignard, L., McDole, K., Wan, Y., Lemon, W. C., Kainmueller, D., Keller, P. J., Preibisch, S., & Funke, J. (2023). Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations. Nature Biotechnology, 41(1), 44–49.
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.
Molina, D. S. P., Loetgering, L., Eschen, W., Limpert, J., & Rothhardt, J. (2023). Broadband ptychography using curved wavefront illumination. Optics Express, 31(16), 26958–26968.
Rädsch, T., Reinke, A., Weru, V., Tizabi, M. D., Schreck, N., Kavur, A. E., Pekdemir, B., Roß, T., Kopp-Schneider, A., & Maier-Hein, L. (2023). Labelling instructions matter in biomedical image analysis. Nature Machine Intelligence, 1–11.
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:2302.01790). arXiv.
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.
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.
Vassileva, M., Motagh, M., Roessner, S., & Xia, Z. (2023). Reactivation of an old landslide in north–central Iran following reservoir impoundment: Results from multisensor satellite time-series analysis. Engineering Geology, 107337.
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.
Wang, W., Motagh, M., Mirzaee, S., Li, T., Zhou, C., Tang, H., & Roessner, S. (2023). The 21 July 2020 Shaziba landslide in China: Results from multi-source satellite remote sensing. Remote Sensing of Environment, 295, 113669.
Weikert, T., Jaeger, P. F., Yang, S., Baumgartner, M., Breit, H. C., Winkel, D. J., Sommer, G., Stieltjes, B., Thaiss, W., Bremerich, J., Maier-Hein, K. H., & Sauter, A. W. (2023). Automated lung cancer assessment on 18F-PET/CT using Retina U-Net and anatomical region segmentation. European Radiology, 33(6), 4270–4279.
Xia, Z., Motagh, M., Li, T., Peng, M., & Roessner, S. (2023). A methodology to characterize 4D post-failure slope instability dynamics using remote sensing measurements: A case study of the Aniangzhai landslide in Sichuan, Southwest China. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 402–414.


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.
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.
Baltruschat, I. M., Cwieka, H., Krüger, D., Zeller-Plumhoff, B., Schlünzen, F., Willumeit-Römer, R., Moosmann, J., & Heuser, P. (2022). Abstract: Verbesserung des 2D U-Nets für die 3D Mikrotomographie mit Synchrotronstrahlung mittels Multi-Axes Fusing. In K. Maier-Hein, T. M. Deserno, H. Handels, A. Maier, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2022 (pp. 128–128). Springer Fachmedien.
Boßmann, F., Krause-Solberg, S., Maly, J., & Sissouno, N. (2022). Structural Sparsity in Multiple Measurements. IEEE Transactions on Signal Processing, 70, 280–291.
Bron, E. E., Klein, S., Reinke, A., Papma, J. M., Maier-Hein, L., Alexander, D. C., & Oxtoby, N. P. (2022). Ten years of image analysis and machine learning competitions in dementia. NeuroImage, 253, 119083.
Collister, J. A., Liu, X., & Clifton, L. (2022). Calculating Polygenic Risk Scores (PRS) in UK Biobank: A Practical Guide for Epidemiologists. Frontiers in Genetics, 13, 818574.
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:2212.08568). arXiv.
Filbir, F., & Melnyk, O. (2022). Image Recovery for Blind Polychromatic Ptychography (arXiv:2210.01626). arXiv.
Gotkowski, K., Gonzalez, C., Kaltenborn, I. J., Fischbach, R., Bucher, A., & Mukhopadhyay, A. (2022, June 22). i3Deep: Efficient 3D interactive segmentation with the nnU-Net. Medical Imaging with Deep Learning.
Grote, L., Seyrich, M., Döhrmann, R., Harouna-Mayer, S. Y., Mancini, F., Kaziukenas, E., Fernandez-Cuesta, I., A. Zito, C., Vasylieva, O., Wittwer, F., Odstrčzil, M., Mogos, N., Landmann, M., Schroer, C. G., & Koziej, D. (2022). Imaging Cu2O nanocube hollowing in solution by quantitative in situ X-ray ptychography. Nature Communications, 13(1), 4971.
Haller, S., Feineis, L., Hutschenreiter, L., Bernard, F., Rother, C., Kainmüller, D., Swoboda, P., & Savchynskyy, B. (2022). A Comparative Study of Graph Matching Algorithms in Computer Vision (arXiv:2207.00291). arXiv.
HIF-EXPLO. (2022). hifexplo/hylite. (Original work published 2020)
Hirsch, P., Malin-Mayor, C., Santella, A., Preibisch, S., Kainmueller, D., & Funke, J. (2022). Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages (arXiv:2208.11467). arXiv.
Inceoglu, F., Shprits, Y. Y., Heinemann, S. G., & Bianco, S. (2022). Identification of Coronal Holes on AIA/SDO Images Using Unsupervised Machine Learning. The Astrophysical Journal, 930(2), 118.
Isensee, F., Ulrich, C., Wald, T., & Maier-Hein, K. H. (2022). Extending nnU-Net is all you need (arXiv:2208.10791). arXiv.
Kabri, S., Auras, A., Riccio, D., Bauermeister, H., Benning, M., Moeller, M., & Burger, M. (2022). Convergent Data-driven Regularizations for CT Reconstruction (arXiv:2212.07786). arXiv.
Kazimi, B., Heuser, P., Schluenzen, F., Cwieka, H., Krüger, D., Zeller-Plumhoff, B., Wieland, F., Hammel, J. U., Beckmann, F., & Moosmann, J. (2022). An active learning approach for the interactive and guided segmentation of tomography data. Developments in X-Ray Tomography XIV, 12242, 79–84.
Klein, L., El-Assady, M., & Jäger, P. F. (2022). From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process (arXiv:2207.04969). arXiv.
Klein, L., Carvalho, J. B. S., El-Assady, M., Penna, P., Buhmann, J. M., & Jaeger, P. F. (2022, June 22). Improving Explainability of Disentangled Representations using Multipath-Attribution Mappings. Medical Imaging with Deep Learning.
Krüger, D., Galli, S., Zeller-Plumhoff, B., Wieland, D. C. F., Peruzzi, N., Wiese, B., Heuser, P., Moosmann, J., Wennerberg, A., & Willumeit-Römer, R. (2022). High-resolution ex vivo analysis of the degradation and osseointegration of Mg-xGd implant screws in 3D. Bioactive Materials, 13, 37–52.
Lyubomirskiy, M., Wittwer, F., Kahnt, M., Koch, F., Kubec, A., Falch, K. V., Garrevoet, J., Seyrich, M., David, C., & Schroer, C. G. (2022). Multi-beam X-ray ptychography using coded probes for rapid non-destructive high resolution imaging of extended samples. Scientific Reports, 12(1), 6203.
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:2206.01653). arXiv.
Meissner, G. W., Nern, A., Dorman, Z., DePasquale, G. M., Forster, K., Gibney, T., Hausenfluck, J. H., He, Y., Iyer, N., Jeter, J., Johnson, L., Johnston, R. M., Lee, K., Melton, B., Yarbrough, B., Zugates, C. T., Clements, J., Goina, C., Otsuna, H., … Team, F. P. (2022). A searchable image resource of Drosophila GAL4-driver expression patterns with single neuron resolution. bioRxiv.
Melnyk, O. (2022). Stochastic Amplitude Flow for phase retrieval, its convergence and doppelg\"angers (arXiv:2212.04916). arXiv.
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:2209.13008). arXiv.
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.
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:2104.05642). arXiv.
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.
Rumberger, J. L., Baumann, E., Hirsch, P., Janowczyk, A., Zlobec, I., & Kainmueller, D. (2022). Panoptic segmentation with highly imbalanced semantic labels (arXiv:2203.11692). arXiv.
Saporta, P., Hajnsek, I., & Alonso-Gonzalez, A. (2022). A temporal assessment of fully polarimetric multifrequency SAR observations over the Canadian permafrost. Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR. European Conference on Synthetic Aperture Radar, EUSAR, Leipzig, Germany.
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.
Scherr, T., Seiffarth, J., Wollenhaupt, B., Neumann, O., Schilling, M. P., Kohlheyer, D., Scharr, H., Nöh, K., & Mikut, R. (2022). microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation. PLOS ONE, 17(11), e0277601.
Seiboth, F., Kubec, A., Schropp, A., Niese, S., Gawlitza, P., Garrevoet, J., Galbierz, V., Achilles, S., Patjens, S., Stuckelberger, M. E., David, C., & Schroer, C. G. (2022). Rapid aberration correction for diffractive X-ray optics by additive manufacturing. Optics Express, 30(18), 31519–31529.
Seiffarth, J., Scherr, T., Wollenhaupt, B., Neumann, O., Scharr, H., Kohlheyer, D., Mikut, R., & Nöh, K. (2022). ObiWan-Microbi: OMERO-based integrated workflow for annotating microbes in the cloud. bioRxiv.
Tran, T. N., Adler, T., Yamlahi, A., Christodoulou, E., Godau, P., Reinke, A., Tizabi, M. D., Sauer, P., Persicke, T., Albert, J. G., & Maier-Hein, L. (2022). Sources of performance variability in deep learning-based polyp detection (arXiv:2211.09708). arXiv.
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.
Wittwer, F., Hagemann, J., Brückner, D., Flenner, S., & Schroer, C. G. (2022). Phase retrieval framework for direct reconstruction of the projected refractive index applied to ptychography and holography. Optica, 9(3), 295–302.
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).
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).
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.
nnU-Net. (2022). MIC-DKFZ. (Original work published 2019)


Albrecht, J. P., Schmidt, D., & Harrington, K. (2021). Album: a framework for scientific data processing with software solutions of heterogeneous tools (arXiv:2110.00601). arXiv.
Alizadehfanaloo, S., Garrevoet, J., Seyrich, M., Murzin, V., Becher, J., Doronkin, D. E., Sheppard, T. L., Grunwaldt, J.-D., Schroer, C. G., & Schropp, A. (2021). Tracking dynamic structural changes in catalysis by rapid 2D-XANES microscopy. Journal of Synchrotron Radiation, 28(5), 1518–1527.
Baltruschat, I. M., Ćwieka, H., Krüger, D., Zeller-Plumhoff, B., Schlünzen, F., Willumeit-Römer, R., Moosmann, J., & Heuser, P. (2021). Scaling the U-net: segmentation of biodegradable bone implants in high-resolution synchrotron radiation microtomograms. Scientific Reports, 11(1), 24237.
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.
Burger, M. (2021). Variational Regularization in Inverse Problems and Machine Learning (arXiv:2112.04591). arXiv.
Godau, P., & Maier-Hein, L. (2021). Task Fingerprinting for Meta Learning inBiomedical Image Analysis. 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. 436–446). Springer International Publishing.
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Other Researches


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).

Image Analysis & Benchmarking

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.

Model-based inverse design

At the beginning of the imaging pipeline is the data acquisition, which measures the change of an emitted signal when interacting with sample. This change can be measured physically on the one hand and modeled mathematically on the other. For a known sample, the response of the physical system can be determined from the model. Far more often, however, one would like to infer the nature of the sample from the measured response. To do this, the mathematical model must be inverted. These so-called inverse problems are at the heart of almost every imaging technique.

Integrative imaging data science

The amount of image data, algorithms and visualization solutions is growing vastly. This results in the urgent demand for integration across multiple modalities and scales in space and time. We develop and provide HIP solutions that can handle the very heterogeneous image data from the research areas of the Helmholtz Association without imposing restrictions on the respective image modalities. To lay the groundwork for the implementation of HIP solutions, our team at MDC will focus on the following research topics:

  1. Develop concepts and algorithms for handling and generic processing of high-dimensional datasets
  2. Develop algorithms for large, high-dimensional image data stitching, fusion and visualization