Publications

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: support@helmholtz-imaging.de

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.

Publications

Adler, T. J., Nölke, J.-H., Reinke, A., Tizabi, M. D., Gruber, S., Trofimova, D., Ardizzone, L., Jaeger, P. F., Buettner, F., Köthe, U., & Maier-Hein, L. (2025). Application-driven validation of posteriors in inverse problems. Medical Image Analysis, 101, 103474. https://doi.org/10.1016/j.media.2025.103474
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
Dora, J., Möddel, M., Flenner, S., Reimers, J., Zeller-Plumhoff, B., Schroer, C. G., Knopp, T., & Hagemann, J. (2025). Model-based autofocus for near-field phase retrieval. Optics Express, 33(4), 6641–6657. https://doi.org/10.1364/OE.544573
Ehrenreich-Petersen, E., Massani, B., Engler, T., Pardo, O. S., Glazyrin, K., Giordano, N., Hagemann, J., Sneed, D., Fedotenko, T., Campbell, D. J., Wendt, M., Wenz, S., Schroer, C. G., Trabs, M., McWilliams, R. S., Liermann, H.-P., Jenei, Z., & O’Bannon, E. F. (2025). X-ray phase contrast imaging and diffraction in the laser-heated diamond anvil cell: A case study on the high-pressure melting of Pt. Results in Physics, 69, 108132. https://doi.org/10.1016/j.rinp.2025.108132
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, 100421. https://doi.org/10.1016/j.jpi.2025.100421
Giese, W., Albrecht, J. P., Oppenheim, O., Akmeriç, E. B., Kraxner, J., Schmidt, D., Harrington, K., & Gerhardt, H. (2025). Polarity-JaM: an image analysis toolbox for cell polarity, junction and morphology quantification. Nature Communications, 16(1), 1474. https://doi.org/10.1038/s41467-025-56643-x
Godau, P., Kalinowski, P., Christodoulou, E., Reinke, A., Tizabi, M., Ferrer, L., Jäger, P., & Maier-Hein, L. (2025). Navigating prevalence shifts in image analysis algorithm deployment. Medical Image Analysis, 102, 103504. https://doi.org/10.1016/j.media.2025.103504
Grünberg, I., Hollenbach Borges, D., Hammar, J., Rutter, N., Marsh, P., & Boike, J. (2025, January 20). Snow on permafrost: the effect of spatial snow variability on soil temperature in Trail Valley Creek, NWT, Canada. https://doi.org/10.5194/egusphere-egu24-17057
Hollenbach Borges, D., Grünberg, I., Hammar, J., Rutter, N., Krumpen, T., & Boike, J. (2025, January 20). Snow accumulation patterns from 2023 Airborne Laser Scanning data in Trail Valley Creek, Western Canadian Arctic. https://doi.org/10.5194/egusphere-egu24-16806
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
Karg, C., Stricker, S., Hutschenreiter, L., Savchynskyy, B., & Kainmueller, D. (2025). Fully Unsupervised Annotation of C. Elegans (arXiv:2503.07348). arXiv. https://doi.org/10.48550/arXiv.2503.07348
Kasahara, K., Seiffarth, J., Stute, B., Lieres, E. von, Drepper, T., Nöh, K., & Kohlheyer, D. (2025). Unveiling microbial single-cell growth dynamics under rapid periodic oxygen oscillations. ChemRxiv. https://doi.org/10.26434/chemrxiv-2025-732st
Kirchner, F., Wieland, D. C. F., Irvine, S., Schimek, S., Reimers, J., Aversa, R., Boubnov, A., Lucas, C., Flenner, S., Greving, I., Marinho, A. L., Wong, T. M., Willumeit-Römer, R., Eschke, C., & Zeller-Plumhoff, B. (2025). An ontology-based description of nano computed tomography measurements in electronic laboratory notebooks: from metadata schema to first user experience (arXiv:2501.07398). arXiv. https://doi.org/10.48550/arXiv.2501.07398
Klein, L., Lüth, C. T., Schlegel, U., Bungert, T. J., El-Assady, M., & Jäger, P. F. (2025). Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics (arXiv:2409.16756). arXiv. https://doi.org/10.48550/arXiv.2409.16756
Pfaehler, E., Pflugfelder, D., & Scharr, H. (2025). Untrained Perceptual Loss for image denoising of line-like structures in MR images (arXiv:2411.05884). arXiv. https://doi.org/10.48550/arXiv.2411.05884
Witting, L., Seiffarth, J., Stute, B., Schulze, T., Hofer, J. M., Nöh, K., Eisenhut, M., Weber, A. P. M., Lieres, E. von, & Kohlheyer, D. (2025). A microfluidic system for the cultivation of cyanobacteria with precise light intensity and CO2 control: enabling growth data acquisition at single-cell resolution. Lab on a Chip, 25(3), 319–329. https://doi.org/10.1039/D4LC00567H
Zimmerer, D., & Maier-Hein, K. (2025). Beyond Heatmaps: A Comparative Analysis of Metrics for Anomaly Localization in Medical Images. In C. H. Sudre, R. Mehta, C. Ouyang, C. Qin, M. Rakic, & W. M. Wells (Eds.), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (pp. 138–148). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-73158-7_13
Afifi, A. J., Thiele, S. T., Rizaldy, A., Lorenz, S., Ghamisi, P., Tolosana-Delgado, R., Kirsch, M., Gloaguen, R., & Heizmann, M. (2024). Tinto: Multisensor Benchmark for 3-D Hyperspectral Point Cloud Segmentation in the Geosciences. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–15. https://doi.org/10.1109/TGRS.2023.3340293
Almeida, S. D., Norajitra, T., Lüth, C. T., Wald, T., Weru, V., Nolden, M., Jäger, P. F., von Stackelberg, O., Heußel, C. P., Weinheimer, O., Biederer, J., Kauczor, H.-U., & Maier-Hein, K. (2024). Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT. European Radiology, 34(7), 4379–4392. https://doi.org/10.1007/s00330-023-10540-3
Almeida, S. D., Norajitra, T., Lüth, C. T., Wald, T., Weru, V., Nolden, M., Jäger, P. F., von Stackelberg, O., Heußel, C. P., Weinheimer, O., Biederer, J., Kauczor, H.-U., & Maier-Hein, K. (2024). Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography. Frontiers in Medicine, 11, 1360706. https://doi.org/10.3389/fmed.2024.1360706
Amara, K., Klein, L., Lüth, C., Jäger, P., Strobelt, H., & El-Assady, M. (2024). Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities (arXiv:2410.01690). arXiv. https://doi.org/10.48550/arXiv.2410.01690
Arteaga Cardona, F., Madirov, E., Popescu, R., Wang, D., Busko, D., Ectors, D., Kübel, C., Eggeler, Y. M., Arús, B. A., Chmyrov, A., Bruns, O. T., Richards, B. S., & Hudry, D. (2024). Dramatic Impact of Materials Combinations on the Chemical Organization of Core–Shell Nanocrystals: Boosting the Tm3+ Emission above 1600 nm. ACS Nano, 18(38), 26233–26250. https://doi.org/10.1021/acsnano.4c07932
Baev, K., Bagschik, K., Bartolini, R., Gühr, M., Klumpp, S., Leemans, W., Maier, A. R., Martinez de la Ossa, A., Osterhoff, J., Reichert, H., Schaper, L., Schreiber, S., Schroer, C. G., Schubert, K., Winkler, P., & Weckert, E. (2024). X-Ray Science at DESY: Upgrade Programs for the User Facilities FLASH and PETRA III. Synchrotron Radiation News, 37(1), 4–11. https://doi.org/10.1080/08940886.2024.2312048
Bailo, R., Barbaro, A., Gomes, S. N., Riedl, K., Roith, T., Totzeck, C., & Vaes, U. (2024). CBX: Python and Julia packages for consensus-based interacting particle methods. Journal of Open Source Software, 9(98), 6611. https://doi.org/10.21105/joss.06611
Bangun, A., Cao, Z., Quercia, A., Scharr, H., & Pfaehler, E. (2024). MRI Reconstruction with Regularized 3D Diffusion Model (R3DM) (arXiv:2412.18723). arXiv. https://doi.org/10.48550/arXiv.2412.18723
Bansal, U., Sharma, A., Putz, B., Kirchlechner, C., & Lee, S. (2024). Data-efficient 4D-STEM in SEM: Beyond 2D Materials to Metallic Materials (arXiv:2410.17206). arXiv. https://doi.org/10.48550/arXiv.2410.17206
Baumann, E., Dislich, B., Rumberger, J. L., Nagtegaal, I. D., Martinez, M. R., & Zlobec, I. (2024, February 13). HoVer-NeXt: A Fast Nuclei Segmentation and Classification Pipeline for Next Generation Histopathology. Medical Imaging with Deep Learning. https://openreview.net/forum?id=3vmB43oqIO
Bounias, D., Baumgartner, M., Neher, P., Kovacs, B., Floca, R., Jaeger, P. F., Kapsner, L. A., Eberle, J., Hadler, D., Laun, F., Ohlmeyer, S., Maier-Hein, K. H., & Bickelhaupt, S. (2024). Abstract: Object Detection for Breast Diffusion-weighted Imaging. In A. Maier, T. M. Deserno, H. Handels, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024 (pp. 334–334). Springer Fachmedien. https://doi.org/10.1007/978-3-658-44037-4_84
Bounias, D., Baumgartner, M., Neher, P., Kovacs, B., Floca, R., Jaeger, P. F., Kapsner, L. A., Eberle, J., Hadler, D., Laun, F., Ohlmeyer, S., Maier-Hein, K. H., & Bickelhaupt, S. (2024). Abstract: Object Detection for Breast Diffusion-weighted Imaging. In A. Maier, T. M. Deserno, H. Handels, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024 (pp. 334–334). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_84
Breimann, L., Bahry, E., Zouinkhi, M., Kolyvanov, K., Street, L. A., Preibisch, S., & Ercan, S. (2024). Analysis of developmental gene expression using smFISH and in silico staging of C. elegans embryos. bioRxiv. https://doi.org/10.1101/2024.05.15.594414
Brokman, J., Burger, M., & Gilboa, G. (2024). Spectral Total-Variation Processing of Shapes - Theory and Applications. ACM Transactions on Graphics. https://doi.org/10.1145/3641845
Bruna, M., Burger, M., & Wit, O. de. (2024). Lane formation and aggregation spots in a model of ants (arXiv:2401.15046). arXiv. https://doi.org/10.48550/arXiv.2401.15046
Bungert, L., Calder, J., & Roith, T. (2024). Ratio convergence rates for Euclidean first-passage percolation: Applications to the graph infinity Laplacian (arXiv:2210.09023). arXiv. https://doi.org/10.48550/arXiv.2210.09023
Burger, M., & Kabri, S. (2024). Learned regularization for inverse problems. In T. A. Bubba (Ed.), Data-driven Models in Inverse Problems (pp. 39–72). De Gruyter. https://doi.org/10.1515/9783111251233-002
Burger, M., Schuster, T., & Wald, A. (2024). Ill-posedness of time-dependent inverse problems in Lebesgue-Bochner spaces. Inverse Problems, 40(8), 085008. https://doi.org/10.1088/1361-6420/ad5a35
Burger, M., Ehrhardt, M. J., Kuger, L., & Weigand, L. (2024). Analysis of Primal-Dual Langevin Algorithms (arXiv:2405.18098). arXiv. https://doi.org/10.48550/arXiv.2405.18098
Burger, M., Loy, N., & Rossi, A. (2024). Asymptotic and stability analysis of kinetic models for opinion formation on networks: an Allen-Cahn approach (arXiv:2407.03375). arXiv. https://doi.org/10.48550/arXiv.2407.03375
Cerda-Jara, C. A., Kim, S. J., Thomas, G., Farsi, Z., Zolotarov, G., Dube, G., Deter, A., Bahry, E., Georgii, E., Woehler, A., Piwecka, M., & Rajewsky, N. (2024). miR-7 controls glutamatergic transmission and neuronal connectivity in a Cdr1as-dependent manner. EMBO Reports, 25(7), 3008–3039. https://doi.org/10.1038/s44319-024-00168-9
Christodoulou, E., Reinke, A., Houhou, R., Kalinowski, P., Erkan, S., Sudre, C. H., Burgos, N., Boutaj, S., Loizillon, S., Solal, M., Rieke, N., Cheplygina, V., Antonelli, M., Mayer, L. D., Tizabi, M. D., Cardoso, M. J., Simpson, A., Jäger, P. F., Kopp-Schneider, A., … Maier-Hein, L. (2024). Confidence intervals uncovered: Are we ready for real-world medical imaging AI? (arXiv:2409.17763). arXiv. https://doi.org/10.48550/arXiv.2409.17763
Cimini, B. A., Bankhead, P., D’Antuono, R., Fazeli, E., Fernandez-Rodriguez, J., Fuster-Barceló, C., Haase, R., Jambor, H. K., Jones, M. L., Jug, F., Klemm, A. H., Kreshuk, A., Marcotti, S., Martins, G. G., McArdle, S., Miura, K., Muñoz-Barrutia, A., Murphy, L. C., Nelson, M. S., … Eliceiri, K. W. (2024). The crucial role of bioimage analysts in scientific research and publication. Journal of Cell Science, 137(20), jcs262322. https://doi.org/10.1242/jcs.262322
D. Almeida, S., Norajitra, T., Lüth, C. T., Wald, T., Weru, V., Nolden, M., Jäger, P. F., von Stackelberg, O., Heußel, C. P., Weinheimer, O., Biederer, J., Kauczor, H.-U., & Maier-Hein, K. (2024). How do deep-learning models generalize across populations? Cross-ethnicity generalization of COPD detection. Insights into Imaging, 15(1), 198. https://doi.org/10.1186/s13244-024-01781-x
Das, S., Kahnt, M., Valen, Y. van, Bergh, T., Blomberg, S., Lyubomirskiy, M., Schroer, C. G., Venvik, H. J., & Sheppard, T. L. (2024). Restructuring of Ag catalysts for methanol to formaldehyde conversion studied using in situ X-ray ptychography and electron microscopy. Catalysis Science & Technology, 14(20), 5885–5898. https://doi.org/10.1039/D4CY00770K
Das, S., Stuckelberger, M. E., Pottbacker, J., Jakobtorweihen, S., Schroer, C. G., Horn, R., & Sheppard, T. L. (2024). Quantitative Chemical Mapping of Pt/Rh Gauze Catalysts for Ammonia Oxidation using Resonant X-ray Tomography. The Journal of Physical Chemistry C, 128(12), 5053–5063. https://doi.org/10.1021/acs.jpcc.4c00041
Deidda, P., Burger, M., Putti, M., & Tudisco, F. (2024). The graph $\infty$-Laplacian eigenvalue problem (arXiv:2410.19666). arXiv. https://doi.org/10.48550/arXiv.2410.19666
Denner, S., Zimmerer, D., Bounias, D., Bujotzek, M., Xiao, S., Kausch, L., Schader, P., Penzkofer, T., Jäger, P. F., & Maier-Hein, K. (2024). Leveraging Foundation Models for Content-Based Medical Image Retrieval in Radiology (arXiv:2403.06567). arXiv. https://doi.org/10.48550/arXiv.2403.06567
Denner, S., Bujotzek, M., Bounias, D., Zimmerer, D., Stock, R., Jäger, P. F., & Maier-Hein, K. (2024). Visual Prompt Engineering for Medical Vision Language Models in Radiology (arXiv:2408.15802). arXiv. https://doi.org/10.48550/arXiv.2408.15802
Dohmen, M., Mittermaier, M., Rumberger, J. L., Yang, L.-L., Gruber, A. D., Toennies, M., Hippenstiel, S., Kainmueller, D., & Hocke, A. C. (2024). Simultaneous Lung Cell and Nucleus Segmentation From Labelled Versus Unlabelled Human Lung DIC Images. 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. https://doi.org/10.1109/ISBI56570.2024.10635198
Dora, J., Möddel, M., Flenner, S., Schroer, C. G., Knopp, T., & Hagemann, J. (2024). Artifact-suppressing reconstruction of strongly interacting objects in X-ray near-field holography without a spatial support constraint. Optics Express, 32(7), 10801–10828. https://doi.org/10.1364/OE.514641
Dora, J., Flenner, S., Lopes Marinho, A., & Hagemann, J. (2024). A Python framework for the online reconstruction of X-ray near-field holography data (1.3.1) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.14024980
Dorent, R., Khajavi, R., Idris, T., Ziegler, E., Somarouthu, B., Jacene, H., LaCasce, A., Deissler, J., Ehrhardt, J., Engelson, S., Fischer, S. M., Gu, Y., Handels, H., Kasai, S., Kondo, S., Maier-Hein, K., Schnabel, J. A., Wang, G., Wang, L., … Kapur, T. (2024). LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification (arXiv:2408.10069). arXiv. https://doi.org/10.48550/arXiv.2408.10069
Ehrhardt, M. J., Kuger, L., & Schönlieb, C.-B. (2024). Proximal Langevin Sampling With Inexact Proximal Mapping. SIAM Journal on Imaging Sciences, 17(3), 1729–1760. https://doi.org/10.1137/23M1593565
Fazeny, A., Tenbrinck, D., Lukin, K., & Burger, M. (2024). Hypergraph p-Laplacians and Scale Spaces. Journal of Mathematical Imaging and Vision, 66(4), 529–549. https://doi.org/10.1007/s10851-024-01183-0
Fazeny, A., Burger, M., & Pietschmann, J.-F. (2024). Optimal transport on gas networks (arXiv:2405.01698). arXiv. https://doi.org/10.48550/arXiv.2405.01698
Fischer, M., Neher, P., Wald, T., Almeida, S. D., Xiao, S., Schüffler, P., Braren, R., Götz, M., Muckenhuber, A., Kleesiek, J., Nolden, M., & Maier-Hein, K. (2024). Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution (arXiv:2406.12623). arXiv. https://doi.org/10.48550/arXiv.2406.12623
Floca, R., Bohn, J., Haux, C., Wiestler, B., Zöllner, F. G., Reinke, A., Weiß, J., Nolden, M., Albert, S., Persigehl, T., Norajitra, T., Baeßler, B., Dewey, M., Braren, R., Büchert, M., Fallenberg, E. M., Galldiks, N., Gerken, A., Götz, M., … Bamberg, F. (2024). Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics. Insights into Imaging, 15(1), 124. https://doi.org/10.1186/s13244-024-01704-w
Franzen, J., Winklmayr, C., Guarino, V. E., Karg, C., Yu, X., Koreuber, N., Albrecht, J. P., Bischoff, P., & Kainmueller, D. (2024). Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification (arXiv:2411.07097). arXiv. https://doi.org/10.48550/arXiv.2411.07097
Friederich, N., Sitcheu, A. J. Y., Nassal, A., Pesch, M., Yildiz, E., Beichter, M., Scholtes, L., Akbaba, B., Lautenschlager, T., Neumann, O., Kohlheyer, D., Scharr, H., Seiffarth, J., Nöh, K., & Mikut, R. (2024). EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis (arXiv:2411.05030). arXiv. https://doi.org/10.48550/arXiv.2411.05030
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
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
Graham, S., Vu, Q. D., Jahanifar, M., Weigert, M., Schmidt, U., Zhang, W., Zhang, J., Yang, S., Xiang, J., Wang, X., Rumberger, J. L., Baumann, E., Hirsch, P., Liu, L., Hong, C., Aviles-Rivero, A. I., Jain, A., Ahn, H., Hong, Y., … Rajpoot, N. M. (2024). CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting. Medical Image Analysis, 92, 103047. https://doi.org/10.1016/j.media.2023.103047
Grünberg, I., Groenke, B., Westermann, S., & Boike, J. (2024). Permafrost and Active Layer Temperature and Freeze/Thaw Timing Reflect Climatic Trends at Bayelva, Svalbard. Journal of Geophysical Research: Earth Surface, 129(7), e2024JF007648. https://doi.org/10.1029/2024JF007648
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
Gutierrez Becker, B., Fraessle, S., Yao, H., Lüscher, J., Girycki, R., Machura, B., Gośliński, J., Czornik, J., Pitura, M., Arús-Pous, J., Fisher, E., Bojic, D., Richmond, D., Bigorgne, A., & Prunotto, M. (2024). P098 The Endoscopic Severity Score Map (ESSM): An Artificial Intelligence scoring system providing accurate, objective and localised measurements of endoscopic disease severity in ulcerative colitis. Journal of Crohn’s and Colitis, 18(Supplement_1), i377–i378. https://doi.org/10.1093/ecco-jcc/jjad212.0228
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
He, H., Fischer, C., Darsow, U., Aguirre, J., & Ntziachristos, V. (2024). Quality control in clinical raster-scan optoacoustic mesoscopy. Photoacoustics, 35, 100582. https://doi.org/10.1016/j.pacs.2023.100582
He, H., Paetzold, J. C., Borner, N., Riedel, E., Gerl, S., Schneider, S., Fisher, C., Ezhov, I., Shit, S., Li, H., Ruckert, D., Aguirre, J., Biedermann, T., Darsow, U., Menze, B., & Ntziachristos, V. (2024). Machine Learning Analysis of Human Skin by Optoacoustic Mesoscopy for Automated Extraction of Psoriasis and Aging Biomarkers. IEEE Transactions on Medical Imaging, 43(6), 2074–2085. https://doi.org/10.1109/TMI.2024.3356180
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
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
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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. https://doi.org/10.1007/978-3-031-31975-4_52
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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. https://doi.org/10.48550/ARXIV.2303.12540
Graf, O., Krahmer, F., & Krause-Solberg, S. (2023). One-bit Sigma-Delta modulation on the circle. Advances in Computational Mathematics, 49(3), 32. https://doi.org/10.1007/s10444-023-10032-4
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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
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. https://doi.org/10.48550/arXiv.2310.12568
Hammar, J., Grünberg, I., Hendricks, S., Jutila, A., Helm, V., & Boike, J. (2023). Snow covered digital elevation model and snow depth product (2019), Trail Valley Creek, NWT, Canada. PANGAEA. https://doi.org/10.1594/PANGAEA.962552
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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
Holzschuh, J., Zimmerer, D., Ulrich, C., Baumgartner, M., Koehler, G., Stiefelhagen, R., & Maier-Hein, K. (2023, April 28). Combining Anomaly Detection and Supervised Learning for Medical Image Segmentation. Medical Imaging with Deep Learning, short paper track. https://openreview.net/forum?id=OytzS_LCWvw
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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. https://doi.org/10.1007/978-3-031-31975-4_18
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. https://www.frontiersin.org/articles/10.3389/fmicb.2023.1198170
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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
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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
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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. https://openreview.net/forum?id=R420Pr5vUj3
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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. https://doi.org/10.3847/1538-4357/ac5f43
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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. https://openreview.net/forum?id=3uQ2Z0MhnoE
Koehler, G., Isensee, F., & Maier-Hein, K. (2022). A Noisy nnU-Net Student for Semi-supervised Abdominal Organ Segmentation. https://openreview.net/forum?id=-XzpY3MyKPU
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. https://doi.org/10.1016/j.bioactmat.2021.10.041
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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.

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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

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