Deep Learning based fully automated volumetric Progression Assessment in Patients with Brain Metastases after stereotactic Radiotherapy

Putz F, Huang Y, Weissmann T, Mansoorian S, Sommer P, Fischer S, Srinivasan A, Frey B, Gaipl U, Schmidt M, Semrau S, Bert C, Fietkau R (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Strahlentherapie und Onkologie Urban & Vogel

Publisher: SPRINGER HEIDELBERG

City/Town: HEIDELBERG

Pages Range: S49-S49

Conference Proceedings Title: STRAHLENTHERAPIE UND ONKOLOGIE

Authors with CRIS profile

Florian Putz Department of Radiation Oncology Yixing Huang Department of Radiation Oncology Thomas Weissmann Department of Radiation Oncology Sina Mansoorian Department of Radiation Oncology Benjamin Frey Department of Radiation Oncology Udo Gaipl Department of Radiation Oncology Sabine Semrau Medizinische Fakultät Christoph Bert Professur für Med. Strahlenphysik Rainer Fietkau Lehrstuhl für Strahlentherapie

How to cite

APA:

Putz, F., Huang, Y., Weissmann, T., Mansoorian, S., Sommer, P., Fischer, S.,... Fietkau, R. (2022). Deep Learning based fully automated volumetric Progression Assessment in Patients with Brain Metastases after stereotactic Radiotherapy. In STRAHLENTHERAPIE UND ONKOLOGIE (pp. S49-S49). HEIDELBERG: SPRINGER HEIDELBERG.

MLA:

Putz, Florian, et al. "Deep Learning based fully automated volumetric Progression Assessment in Patients with Brain Metastases after stereotactic Radiotherapy." Proceedings of the STRAHLENTHERAPIE UND ONKOLOGIE HEIDELBERG: SPRINGER HEIDELBERG, 2022. S49-S49.

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