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

2024

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
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
Burger, M., & Kabri, S. (2024). Learned Regularization for Inverse Problems: Insights from a Spectral Model (arXiv:2312.09845). arXiv. http://arxiv.org/abs/2312.09845
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. https://doi.org/10.1101/2024.01.24.577027
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
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
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. https://doi.org/10.48550/ARXIV.2401.08501
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. https://openaccess.thecvf.com/content/WACV2024/html/Kohler_RecycleNet_Latent_Feature_Recycling_Leads_to_Iterative_Decision_Refinement_WACV_2024_paper.html
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. https://doi.org/10.1101/2024.01.05.574336
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. https://doi.org/10.48550/arXiv.2404.00130
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. https://doi.org/10.48550/arXiv.2311.13964
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. https://doi.org/10.1038/s41596-024-00957-5
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. https://doi.org/10.1038/s41596-024-00957-5
Ossig, C., Strelow, C., Flügge, J., Patjens, S., Garrevoet, J., Spiers, K., Barp, J. L., Hagemann, J., Seiboth, F., De Bastiani, M., Aydin, E., Isikgor, F. H., De Wolf, S., Falkenberg, G., Mews, A., Schroer, C. G., Kipp, T., & Stuckelberger, M. E. (2024). Novel Detection Scheme for Temporal and Spectral X-Ray Optical Analysis: Study of Triple-Cation Perovskites. PRX Energy, 3(2), 023011. https://doi.org/10.1103/PRXEnergy.3.023011
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. https://doi.org/10.1038/s41592-023-02150-0
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. https://doi.org/10.48550/arXiv.2312.17670

2023

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
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. https://elib.dlr.de/199938/
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. https://doi.org/10.48550/arXiv.2309.09764
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. https://doi.org/10.48550/arXiv.2307.07254
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. https://doi.org/10.1007/s00330-023-10540-3
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. https://doi.org/10.1038/s41467-023-40031-4
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. https://doi.org/10.1126/sciadv.add6778
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. https://doi.org/10.1117/12.2673838
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. https://doi.org/10.1016/j.media.2022.102680
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). https://openreview.net/forum?id=WwceaG9wOU#all
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. https://doi.org/10.1007/s00464-023-10447-6
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. https://doi.org/10.1038/s41467-023-40564-8
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. https://doi.org/10.1088/1748-0221/18/11/P11025
Bungert, T. J., Kobelke, L., & Jaeger, P. F. (2023). Understanding Silent Failures in Medical Image Classification. https://doi.org/10.48550/ARXIV.2307.14729
Burger, M., & Esposito, A. (2023). Porous medium equation and cross-diffusion systems as limit of nonlocal interaction. Nonlinear Analysis, 235, 113347. https://doi.org/10.1016/j.na.2023.113347
Burger, M., & Schulz, S. (2023). Well-posedness and stationary states for a crowded active Brownian system with size-exclusion (arXiv:2309.17326). arXiv. https://doi.org/10.48550/arXiv.2309.17326
Burger, M., Schuster, T., & Wald, A. (2023). Ill-posedness of time-dependent inverse problems in Lebesgue-Bochner spaces (arXiv:2310.08600). arXiv. http://arxiv.org/abs/2310.08600
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. https://doi.org/10.48550/arXiv.2209.04677
Burger, M., Humpert, I., & Pietschmann, J.-F. (2023). Dynamic Optimal Transport on Networks. ESAIM: Control, Optimisation and Calculus of Variations, 29, 54. https://doi.org/10.1051/cocv/2023027
Cersovsky, J., Mohammadi, S., Kainmueller, D., & Hoehne, J. (2023). Towards Hierarchical Regional Transformer-based Multiple Instance Learning (arXiv:2308.12634). arXiv. https://doi.org/10.48550/arXiv.2308.12634
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. https://doi.org/10.1007/978-3-031-43993-3_63
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. https://doi.org/10.5281/zenodo.10184579
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. https://doi.org/10.1021/acsnanoscienceau.3c00020
Crick BioImage Analysis Symposium. (2023, November 29). CBIAS 2023 - Lucas von Chamier - Style transfer and artefact-free stitching for generative AI... https://www.youtube.com/watch?v=Rplr6UbxLlM
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. https://doi.org/10.1088/1748-0221/18/10/P10017
Ehrhardt, M. J., Kuger, L., & Schönlieb, C.-B. (2023). Proximal Langevin Sampling With Inexact Proximal Mapping (arXiv:2306.17737). arXiv. https://doi.org/10.48550/arXiv.2306.17737
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. https://doi.org/10.48550/arXiv.2303.17719
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
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. https://doi.org/10.1016/j.jsb.2023.107990
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. 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
Granero, A. C. (2023). UTILE-Oxy - Deep Learning to Automate Video Analysis of Bubble Dynamics in Proton Exchange Membrane Electrolyzers. https://github.com/andyco98/UTILE-Oxy (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. https://doi.org/10.1038/s41598-022-26877-6
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
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. https://doi.org/10.5194/tc-17-5357-2023
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
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. https://doi.org/10.1007/978-3-658-41657-7_39
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. https://doi.org/10.48550/arXiv.2309.13747
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. https://doi.org/10.1007/978-3-658-41657-7_7
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. https://doi.org/10.48550/arXiv.2211.15259
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
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. https://doi.org/10.1139/cjp-2023-0078
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. https://doi.org/10.1002/adma.202307160
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
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. https://doi.org/10.48550/arXiv.2312.02608

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
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. https://www.frontiersin.org/articles/10.3389/fcomp.2022.777728
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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2021

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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: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
Isensee, F., Jaeger, P. F., Full, P. M., Vollmuth, P., & Maier-Hein, K. H. (2020). nnU-Net for Brain Tumor Segmentation (arXiv:2011.00848). 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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