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

4725570 2025 1 https://helmholtz-imaging.de/apa-bold-title.csl 50 creator asc 4420 https://helmholtz-imaging.de/wp-content/plugins/zotpress/
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4725570 2022 1 https://helmholtz-imaging.de/apa-bold-title.csl 50 creator asc 4420 https://helmholtz-imaging.de/wp-content/plugins/zotpress/
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4725570 2021 1 https://helmholtz-imaging.de/apa-bold-title.csl 50 creator asc 4420 https://helmholtz-imaging.de/wp-content/plugins/zotpress/
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Cole, J. H. (2020). Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors. Neurobiology of Aging, 92, 34–42. https://doi.org/10.1016/j.neurobiolaging.2020.03.014
Full, P. M., Isensee, F., Jäger, P. F., & Maier-Hein, K. (2020). Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI (arXiv:2011.07592). arXiv. https://doi.org/10.48550/arXiv.2011.07592
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. https://proceedings.mlr.press/v121/hirsch20a.html
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Kickingereder, P., Brugnara, G., Hansen, M. B., Nowosielski, M., Pflüger, I., Schell, M., Isensee, F., Foltyn, M., Neuberger, U., Kessler, T., Sahm, F., Wick, A., Heiland, S., Weller, M., Platten, M., von Deimling, A., Maier-Hein, K. H., Østergaard, L., van den Bent, M. J., … Bendszus, M. (2020). Noninvasive Characterization of Tumor Angiogenesis and Oxygenation in Bevacizumab-treated Recurrent Glioblastoma by Using Dynamic Susceptibility MRI: Secondary Analysis of the European Organization for Research and Treatment of Cancer 26101 Trial. Radiology, 297(1), 164–175. https://doi.org/10.1148/radiol.2020200978
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Other Researches


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).

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