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: helpdesk@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

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. https://doi.org/10.1038/s41598-022-26877-6
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. https://doi.org/10.1038/s41587-022-01427-7
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. https://doi.org/10.1038/s42256-023-00625-5
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. http://arxiv.org/abs/2302.01790
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. https://doi.org/10.1016/j.media.2023.102765

2022

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. https://doi.org/10.1038/s41467-022-30695-9
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
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. https://doi.org/10.1007/978-3-658-36932-3_28
Boßmann, F., Krause-Solberg, S., Maly, J., & Sissouno, N. (2022). Structural Sparsity in Multiple Measurements. IEEE Transactions on Signal Processing, 70, 280–291. https://doi.org/10.1109/TSP.2021.3137599
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. https://doi.org/10.1016/j.neuroimage.2022.119083
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. https://doi.org/10.3389/fgene.2022.818574
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. https://doi.org/10.48550/arXiv.2212.08568
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
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. https://doi.org/10.1038/s41467-022-32373-2
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. https://doi.org/10.48550/arXiv.2207.00291
HIF-EXPLO. (2022). hifexplo/hylite. https://github.com/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. https://doi.org/10.48550/arXiv.2208.11467
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
Isensee, F., Ulrich, C., Wald, T., & Maier-Hein, K. H. (2022). Extending nnU-Net is all you need. arXiv. https://doi.org/10.48550/arXiv.2208.10791
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. https://doi.org/10.1117/12.2637973
Klein, L., El-Assady, M., & Jäger, P. F. (2022). From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process. arXiv. https://doi.org/10.48550/arXiv.2207.04969
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
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
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. https://doi.org/10.1038/s41598-022-09466-5
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. https://doi.org/10.48550/arXiv.2206.01653
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. https://doi.org/10.1101/2020.05.29.080473
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. https://doi.org/10.48550/arXiv.2209.13008
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. https://doi.org/10.1093/noajnl/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. https://doi.org/10.48550/arXiv.2104.05642
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. https://doi.org/10.1016/j.media.2022.102605
Rumberger, J. L., Baumann, E., Hirsch, P., Janowczyk, A., Zlobec, I., & Kainmueller, D. (2022). Panoptic segmentation with highly imbalanced semantic labels. arXiv. https://doi.org/10.48550/arXiv.2203.11692
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. https://elib.dlr.de/186412/
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. https://doi.org/10.1016/j.pacs.2022.100341
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. https://doi.org/10.1364/OE.454863
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. https://doi.org/10.48550/arXiv.2211.09708
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. https://doi.org/10.1093/neuonc/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. https://doi.org/10.1364/OPTICA.447021
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). https://doi.org/10.1183/13993003.congress-2022.407
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). https://doi.org/10.1183/23120541.LSC-2022.190
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. https://doi.org/10.1109/TMI.2022.3170077
nnU-Net. (2022). MIC-DKFZ. https://github.com/MIC-DKFZ/nnUNet (Original work published 2019)

2021

Albrecht, J. P., Schmidt, D., & Harrington, K. (2021). Album: a framework for scientific data processing with software solutions of heterogeneous tools. arXiv. https://doi.org/10.48550/arXiv.2110.00601
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. https://doi.org/10.1107/S1600577521007074
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. https://doi.org/10.1038/s41598-021-03542-y
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. https://doi.org/10.1007/978-3-030-87240-3_51
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. https://doi.org/10.1007/978-3-030-87202-1_42
Gonstalla, E., Grünberg, I., & Boike, J. (2021). Das Eisbuch - Alles, was man wissen muss, in 50 Grafiken. oekom Verlag.
Haagmans, V. J. T. (2021). Modelling the significance of snow-vegetation interactions for active layer dynamics in an Arctic permafrost region subjected to tundra shrubification [Master, Eidgenössische Technische Hochschule Zürich]. https://www.research-collection.ethz.ch/handle/20.500.11850/518127
Hutschenreiter, L., Haller, S., Feineis, L., Rother, C., Kainmuller, D., & Savchynskyy, B. (2021). Fusion Moves for Graph Matching. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 6250–6259. https://doi.org/10.1109/ICCV48922.2021.00621
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211. https://doi.org/10.1038/s41592-020-01008-z
Iwen, M. A., Krahmer, F., Krause-Solberg, S., & Maly, J. (2021). On Recovery Guarantees for One-Bit Compressed Sensing on Manifolds. Discrete & Computational Geometry, 65(4), 953–998. https://doi.org/10.1007/s00454-020-00267-z
Kahnt, M., Grote, L., Brückner, D., Seyrich, M., Wittwer, F., Koziej, D., & Schroer, C. G. (2021). Multi-slice ptychography enables high-resolution measurements in extended chemical reactors. Scientific Reports, 11(1), 1500. https://doi.org/10.1038/s41598-020-80926-6
Kirsch, M., Lorenz, S., Thiele, S., & Gloaguen, R. (2021). Characterisation of Massive Sulphide Deposits in the Iberian Pyrite Belt Based on the Integration of Digital Outcrops and Multi-Scale, Multi-Source Hyperspectral Data. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 126–129. https://doi.org/10.1109/IGARSS47720.2021.9554149
Lange, S., Grünberg, I., Anders, K., Hartmann, J., Helm, V., & Boike, J. (2021). Airborne Laser Scanning (ALS) Point Clouds of Trail Valley Creek, NWT, Canada (2018). https://doi.org/10.1594/PANGAEA.934387
Li, L., & Heizmann, M. (2021). 2.5D-VoteNet: Depth Map based 3D Object Detection for Real-Time Applications. The 32nd British Machine Vision Conference 2021, 1. https://publikationen.bibliothek.kit.edu/1000140306
Lyubomirskiy, M., Schurink, B., Makhotkin, I. A., Brueckner, D., Brueckner, D., Brueckner, D., Wittwer, F., Wittwer, F., Kahnt, M., Seyrich, M., Seyrich, M., Seiboth, F., Bijkerk, F., Schroer, C. G., & Schroer, C. G. (2021). Planar refractive lenses made of SiC for high intensity nanofocusing. Optics Express, 29(9), 14025–14032. https://doi.org/10.1364/OE.416223
Maier-Hein, L., Wagner, M., Ross, T., Reinke, A., Bodenstedt, S., Full, P. M., Hempe, H., Mindroc-Filimon, D., Scholz, P., Tran, T. N., Bruno, P., Kisilenko, A., Müller, B., Davitashvili, T., Capek, M., Tizabi, M., Eisenmann, M., Adler, T. J., Gröhl, J., … Müller-Stich, B. P. (2021). Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room. arXiv. https://doi.org/10.48550/arXiv.2005.03501
Mais, L., Hirsch, P., Managan, C., Wang, K., Rokicki, K., Svirskas, R. R., Dickson, B. J., Korff, W., Rubin, G. M., Ihrke, G., Meissner, G. W., & Kainmueller, D. (2021). PatchPerPixMatch for Automated 3d Search of Neuronal Morphologies in Light Microscopy. bioRxiv. https://doi.org/10.1101/2021.07.23.453511
Moebel, E., Martinez-Sanchez, A., Lamm, L., Righetto, R. D., Wietrzynski, W., Albert, S., Larivière, D., Fourmentin, E., Pfeffer, S., Ortiz, J., Baumeister, W., Peng, T., Engel, B. D., & Kervrann, C. (2021). Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms. Nature Methods, 18(11), 1386–1394. https://doi.org/10.1038/s41592-021-01275-4
Nill, L. (2021). Revealing Spatio-Temporal Dynamics of Arctic Shrub Expansion: Utilizing Vegetation Cover Fractions from Landsat Time Series [Master, Geographisches Institut der Humboldt-Universität zu Berlin]. https://epic.awi.de/id/eprint/54895/
Reinke, A. (2021, October 6). A discovery dive into the world of evaluation — Do’s don’ts and other considerations. MICCAI Educational Initiative. https://medium.com/miccai-educational-initiative/a-discovery-dive-into-the-world-of-evaluation-dos-don-ts-and-other-considerations-4189ab46fe06
Roß, T., Reinke, A., Full, P. M., Wagner, M., Kenngott, H., Apitz, M., Hempe, H., Mindroc-Filimon, D., Scholz, P., Tran, T. N., Bruno, P., Arbeláez, P., Bian, G.-B., Bodenstedt, S., Bolmgren, J. L., Bravo-Sánchez, L., Chen, H.-B., González, C., Guo, D., … Maier-Hein, L. (2021). Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge. Medical Image Analysis, 70, 101920. https://doi.org/10.1016/j.media.2020.101920
Rumberger, J. L., Yu, X., Hirsch, P., Dohmen, M., Guarino, V. E., Mokarian, A., Mais, L., Funke, J., & Kainmueller, D. (2021). How Shift Equivariance Impacts Metric Learning for Instance Segmentation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 7108–7116. https://doi.org/10.1109/ICCV48922.2021.00704
Schambach, M., Shi, J., & Heizmann, M. (2021). Spectral Reconstruction and Disparity from Spatio-Spectrally Coded Light Fields via Multi-Task Deep Learning. 2021 International Conference on 3D Vision (3DV), 186–196. https://doi.org/10.1109/3DV53792.2021.00029
Schambach, Maximilian. (2021). A highly textured multispectral light field dataset. Karlsruhe Institute of Technology (KIT). https://radar.kit.edu/radar/en/dataset/dPmvqVtGHZHdCurj
Seiffarth, J., & Nöh, K. (2021). SegUI: Creating high-quality image annotation data sets in microbial bioimaging. https://juser.fz-juelich.de/record/902893
Sobolev, E., Heuser, P., Lamzin, V. S., & IUCr. (2021, August 14). Macromolecular model building over the web [Text]. Acta Crystallographica Section A: Foundations and Advances. https://scripts.iucr.org/cgi-bin/paper?S0108767321090267
Thiele, S., Lorenz, S., Bnoulkacem, Z., Kirsch, M., & Gloaguen, R. (2021). Hyperspectral mineral mapping of cliffs using a UAV mounted Hyspex Mjolnir VNIR-SWIR sensor. 2021, 1–3. https://doi.org/10.3997/2214-4609.2021629011
Thiele, S. T., Lorenz, S., Kirsch, M., Cecilia Contreras Acosta, I., Tusa, L., Herrmann, E., Möckel, R., & Gloaguen, R. (2021). Multi-scale, multi-sensor data integration for automated 3-D geological mapping. Ore Geology Reviews, 136, 104252. https://doi.org/10.1016/j.oregeorev.2021.104252
Vassholz, M., Hoeppe, H. P., Hagemann, J., Rosselló, J. M., Osterhoff, M., Mettin, R., Kurz, T., Schropp, A., Seiboth, F., Schroer, C. G., Scholz, M., Möller, J., Hallmann, J., Boesenberg, U., Kim, C., Zozulya, A., Lu, W., Shayduk, R., Schaffer, R., … Salditt, T. (2021). Pump-probe X-ray holographic imaging of laser-induced cavitation bubbles with femtosecond FEL pulses. Nature Communications, 12(1), 3468. https://doi.org/10.1038/s41467-021-23664-1
Wiesenfarth, M., Reinke, A., Landman, B. A., Eisenmann, M., Saiz, L. A., Cardoso, M. J., Maier-Hein, L., & Kopp-Schneider, A. (2021). Methods and open-source toolkit for analyzing and visualizing challenge results. Scientific Reports, 11(1), 2369. https://doi.org/10.1038/s41598-021-82017-6
Wittwer, F., Lyubomirskiy, M., Koch, F., Kahnt, M., Seyrich, M., Garrevoet, J., David, C., & Schroer, C. G. (2021). Upscaling of multi-beam x-ray ptychography for efficient x-ray microscopy with high resolution and large field of view. Applied Physics Letters, 118(17), 171102. https://doi.org/10.1063/5.0045571
Yang, X., & Schroer, C. (2021). Strategies of Deep Learning for Tomographic Reconstruction. 2021 IEEE International Conference on Image Processing (ICIP), 3473–3476. https://doi.org/10.1109/ICIP42928.2021.9506395

2020

Full, P. M., Isensee, F., Jäger, P. F., & Maier-Hein, K. (2020). Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI. 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
Isensee, F., Jaeger, P. F., Full, P. M., Vollmuth, P., & Maier-Hein, K. H. (2020). nnU-Net for Brain Tumor Segmentation. arXiv. https://doi.org/10.48550/arXiv.2011.00848
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
Maier-Hein, L., Reinke, A., Kozubek, M., Martel, A. L., Arbel, T., Eisenmann, M., Hanbury, A., Jannin, P., Müller, H., Onogur, S., Saez-Rodriguez, J., van Ginneken, B., Kopp-Schneider, A., & Landman, B. A. (2020). BIAS: Transparent reporting of biomedical image analysis challenges. Medical Image Analysis, 66, 101796. https://doi.org/10.1016/j.media.2020.101796
Mais, L., Hirsch, P., & Kainmueller, D. (2020). PatchPerPix for Instance Segmentation. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision – ECCV 2020 (pp. 288–304). Springer International Publishing. https://doi.org/10.1007/978-3-030-58595-2_18
Müller, A., Schmidt, D., Xu, C. S., Pang, S., D’Costa, J. V., Kretschmar, S., Münster, C., Kurth, T., Jug, F., Weigert, M., Hess, H. F., & Solimena, M. (2020). 3D FIB-SEM reconstruction of microtubule–organelle interaction in whole primary mouse β cells. Journal of Cell Biology, 220(2), e202010039. https://doi.org/10.1083/jcb.202010039
Schambach, M., & Heizmann, M. (2020). A Multispectral Light Field Dataset and Framework for Light Field Deep Learning. IEEE Access, 8, 193492–193502. https://doi.org/10.1109/ACCESS.2020.3033056
Scheffer, L. K., Xu, C. S., Januszewski, M., Lu, Z., Takemura, S., Hayworth, K. J., Huang, G. B., Shinomiya, K., Maitlin-Shepard, J., Berg, S., Clements, J., Hubbard, P. M., Katz, W. T., Umayam, L., Zhao, T., Ackerman, D., Blakely, T., Bogovic, J., Dolafi, T., … Plaza, S. M. (2020). A connectome and analysis of the adult Drosophila central brain. ELife, 9, e57443. https://doi.org/10.7554/eLife.57443
Schell, M., Pflüger, I., Brugnara, G., Isensee, F., Neuberger, U., Foltyn, M., Kessler, T., Sahm, F., Wick, A., Nowosielski, M., Heiland, S., Weller, M., Platten, M., Maier-Hein, K. H., Von Deimling, A., Van Den Bent, M. J., Gorlia, T., Wick, W., Bendszus, M., & Kickingereder, P. (2020). Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial. Neuro-Oncology, 22(11), 1667–1676. https://doi.org/10.1093/neuonc/noaa120
Zimmerer, D., Isensee, F., Petersen, J., Kohl, S., & Maier-Hein, K. (2020). Abstract: Unsupervised Anomaly Localization Using Variational Auto-Encoders. In T. Tolxdorff, T. M. Deserno, H. Handels, A. Maier, K. H. Maier-Hein, & C. Palm (Eds.), Bildverarbeitung für die Medizin 2020 (pp. 199–199). Springer Fachmedien. https://doi.org/10.1007/978-3-658-29267-6_43

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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).

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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.

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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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