Deep Learning Compatible Differentiable X-ray Projections for Inverse Rendering

Shetty K, Birkhold A, Strobel N, Jaganathan S, Kowarschik M, Maier A, Egger B (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer Science and Business Media Deutschland GmbH

Pages Range: 290-295

Conference Proceedings Title: Informatik aktuell

Event location: Regensburg DE

ISBN: 9783658331979

DOI: 10.1007/978-3-658-33198-6_70

Authors with CRIS profile

Karthik Shetty Lehrstuhl für Informatik 5 (Mustererkennung) Srikrishna Jaganathan Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Bernhard Egger

Additional Organisation(s)

Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)

Involved external institutions

Siemens AG DE Germany (DE) Fraunhofer-Institut für Biomedizinische Technik (IBMT) / Fraunhofer Institute for Biomedical Engineering DE Germany (DE) Massachusetts Institute of Technology (MIT) US United States (USA) (US)

How to cite

APA:

Shetty, K., Birkhold, A., Strobel, N., Jaganathan, S., Kowarschik, M., Maier, A., & Egger, B. (2021). Deep Learning Compatible Differentiable X-ray Projections for Inverse Rendering. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 290-295). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.

MLA:

Shetty, Karthik, et al. "Deep Learning Compatible Differentiable X-ray Projections for Inverse Rendering." Proceedings of the German Workshop on Medical Image Computing, 2021, Regensburg Ed. Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2021. 290-295.

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