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., 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.
Brokman, J., Burger, M., & Gilboa, G. (2024). Spectral Total-Variation Processing of Shapes - Theory and Applications. ACM Transactions on Graphics.
Giese, W., Albrecht, J. P., Oppenheim, O., Akmeriç, E. B., Kraxner, J., Schmidt, D., Harrington, K., & Gerhardt, H. (2024). Polarity-JaM: An image analysis toolbox for cell polarity, junction and morphology quantification. bioRxiv.
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.
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.
Kahl, K.-C., Lüth, C. T., Zenk, M., Maier-Hein, K., & Jaeger, P. F. (2024). ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation.
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.
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.
Mais, L., Hirsch, P., Managan, C., Kandarpa, R., Rumberger, J. L., Reinke, A., Maier-Hein, L., Ihrke, G., & Kainmueller, D. (2024). FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures (arXiv:2404.00130). arXiv.
Marinov, Z., Jäger, P. F., Egger, J., Kleesiek, J., & Stiefelhagen, R. (2024). Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy (arXiv:2311.13964). arXiv.
Müller, A., Schmidt, D., Albrecht, J. P., Rieckert, L., Otto, M., Galicia Garcia, L. E., Fabig, G., Solimena, M., & Weigert, M. (2024). Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets. Nature Protocols, 1–31.
Müller, A., Schmidt, D., Albrecht, J. P., Rieckert, L., Otto, M., Galicia Garcia, L. E., Fabig, G., Solimena, M., & Weigert, M. (2024). Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets. Nature Protocols, 1–31.
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.
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.


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.
Abele, D., Basermann, A., Bungartz, H.-J., & Humbert, A. (2023, September). Inverse Level-set Problems for Capturing Calving Fronts. 11th Applied Inverse Problems Conference. 11th Applied Inverse Problems Conference, Göttingen, Germany.
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. (2023). Application-driven Validation of Posteriors in Inverse Problems (arXiv:2309.09764). 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.
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. (2023). Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT. European Radiology.
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.
Ayala, L., Adler, T. J., Seidlitz, S., Wirkert, S., Engels, C., Seitel, A., Sellner, J., Aksenov, A., Bodenbach, M., Bader, P., Baron, S., Vemuri, A., Wiesenfarth, M., Schreck, N., Mindroc, D., Tizabi, M., Pirmann, S., Everitt, B., Kopp-Schneider, A., … Maier-Hein, L. (2023). Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery. Science Advances, 9(10), eadd6778.
Baumgartner, M., Full, P., & Maier-Hein, K. (2023). Accurate Detection of Mediastinal Lesions with nnDetection.
Bihler, M., Roming, L., Jiang, Y., Afifi, A. J., Aderhold, J., Čibiraitė-Lukenskienė, D., Lorenz, S., Gloaguen, R., Gruna, R., & Heizmann, M. (2023). Multi-sensor data fusion using deep learning for bulky waste image classification. Automated Visual Inspection and Machine Vision V, 12623, 69–82.
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.
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).
Brandenburg, J. M., Jenke, A. C., Stern, A., Daum, M. T. J., Schulze, A., Younis, R., Petrynowski, P., Davitashvili, T., Vanat, V., Bhasker, N., Schneider, S., Mündermann, L., Reinke, A., Kolbinger, F. R., Jörns, V., Fritz-Kebede, F., Dugas, M., Maier-Hein, L., Klotz, R., … Wagner, M. (2023). Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study. Surgical Endoscopy, 37(11), 8577–8593.
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.
Buhmann, E., Diefenbacher, S., Eren, E., Gaede, F., Kasicezka, G., Korol, A., Korcari, W., Krüger, K., & McKeown, P. (2023). CaloClouds: fast geometry-independent highly-granular calorimeter simulation. Journal of Instrumentation, 18(11), P11025.
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.
Burger, M., Humpert, I., & Pietschmann, J.-F. (2023). Dynamic Optimal Transport on Networks. ESAIM: Control, Optimisation and Calculus of Variations, 29, 54.
Cersovsky, J., Mohammadi, S., Kainmueller, D., & Hoehne, J. (2023). Towards Hierarchical Regional Transformer-based Multiple Instance Learning (arXiv:2308.12634). arXiv.
Chobola, T., Müller, G., Dausmann, V., Theileis, A., Taucher, J., Huisken, J., & Peng, T. (2023). LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network. In H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (pp. 656–665). Springer Nature Switzerland.
Colliard-Granero, A., Rodenbücher, C., Gompou, K. A., Malek, K., Eslamibidgoli, M. J., & Michael, E. (2023). Polymer Electrolyte Membrane Water Electrolyzer Oxygen Bubble Evolution Optical Video Recording For Deep Learning-Enhanced Characterization of Bubble Dynamics in Proton Exchange Membrane Water Electrolyzer by André Colliard-Granero, Keusra A. Gompou, Christian Rodenbücher, Kourosh Malek, Michael H. Eikerling, and Mohammad J. Eslamibidgoli. Zenodo.
Colliard-Granero, A., Jitsev, J., Eikerling, M. H., Malek, K., & Eslamibidgoli, M. J. (2023). UTILE-Gen: Automated Image Analysis in Nanoscience Using Synthetic Dataset Generator and Deep Learning. ACS Nanoscience Au, 3(5), 398–407.
Crick BioImage Analysis Symposium. (2023, November 29). CBIAS 2023 - Lucas von Chamier - Style transfer and artefact-free stitching for generative AI...
Diefenbacher, S., Eren, E., Gaede, F., Kasieczka, G., Krause, C., Shekhzadeh, I., & Shih, D. (2023). L2LFlows: generating high-fidelity 3D calorimeter images. Journal of Instrumentation, 18(10), P10017.
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.
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.
Hammar, J., Grünberg, I., Kokelj, S. V., van der Sluijs, J., & Boike, J. (2023). Snow accumulation, albedo and melt patterns following road construction on permafrost, Inuvik–Tuktoyaktuk Highway, Canada. The Cryosphere, 17(12), 5357–5372.
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.
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.
Ickler, M. K., Baumgartner, M., Roy, S., Wald, T., & Maier-Hein, K. H. (2023). Taming Detection Transformers for Medical Object Detection. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 183–188). Springer Fachmedien.
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.
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.
Jaeger, P. F., Lüth, C. T., Klein, L., & Bungert, T. J. (2023). A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification (arXiv:2211.15259). 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.
Ketenoglu, B., Bostanci, E., Ketenoglu, D., Canbay, A. C., Harder, M., Karaca, A. S., Eren, E., Aydin, A., Yin, Z., Guzel, M. S., & Martins, M. (2023). A dedicated application of evolutionary algorithms: synchrotron X-ray radiation optimization based on an in-vacuum undulator. Canadian Journal of Physics.
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.
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.
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.


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.
Arzt, M., Deschamps, J., Schmied, C., Pietzsch, T., Schmidt, D., Tomancak, P., Haase, R., & Jug, F. (2022). LABKIT: Labeling and Segmentation Toolkit for Big Image Data. Frontiers in Computer Science, 4.
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.
Koehler, G., Isensee, F., & Maier-Hein, K. (2022). A Noisy nnU-Net Student for Semi-supervised Abdominal Organ Segmentation.
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.
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Hirsch, P., & Kainmueller, D. (2020). An Auxiliary Task for Learning Nuclei Segmentation in 3D Microscopy Images. Proceedings of the Third Conference on Medical Imaging with Deep Learning, 304–321.
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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