Dr. Philipp Heuser

Head of Support Team at DESY

Philipp has his background in molecular computational biology. He obtained his diploma and PhD in biology from the University of Cologne. During this time, he focused on the prediction of protein structures and their interactions. After this, he joined the European Molecular Biology Laboratory (EMBL) in Hamburg, to work on a software called ARP/wARP for building macromolecular models into electron density maps. In 2018 Philipp joined DESY in the context of the Helmholtz Analytics Framework HAF, where he deepened his knowledge in deep learning for image analysis.

Today Philipp is coordinating the Helmholtz Imaging Engineering and Support Team at DESY. Together with his team he offers support and services to the Helmholtz Imaging Community.

The most prominent service offered is Helmholtz Imaging CONNECT. The support team also works on requests submitted to the Support Hub, is involved in collaborations, and directly supports Helmholtz Imaging Projects.

Beyond that, Philipp is actively involved in collaborative projects with partners from the DESY campus and from the Helmholtz Association developing deep learning tools and pipelines for the semantic and instance segmentation of synchrotron micro CT data, and for the automatisation of cryo electron tomography image processing.

Publications

Moosmann, J. P., Ahrens, J., Irvine, S., Wong, T., Lucas, C., Beckmann, F., Hammel, J. U., Wieland, F., Zeller-Plumhoff, B., & Heuser, P. (2024). Machine learning for the reconstruction and analysis of synchrotron-radiation tomography data. In B. Müller & G. Wang (Eds.), Developments in X-Ray Tomography XV (p. 33). SPIE. https://doi.org/10.1117/12.3028018

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

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

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

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

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

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

Dr. Philipp Heuser

Publications

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