Georgios Tsekas obtained his diploma (BSc and MSc) in Electrical Engineering and Computer Science at the University of Patras, Greece. He continued his studies and obtained his MSc in Biomedical Computing at the Technical University of Munich, conducting his MSc thesis on the topic “An active deep learning framework for muscle segmentation in MSOT images”. During his study period, he gained experience in applying artificial intelligence techniques in biomedical applications, working as an intern at the Institute of Computational Biology and Institute of Radiation Medicine of the Helmholtz Zentrum München.

In 2020 he got employed as a PhD candidate at the UMC Utrecht, under the supervision of Charis Kontaxis, Gijsbert Bol and Bas Raaymakers. His main research includes treatment plan optimization and dose calculation algorithms for prostate IMRT with the MR-Linac.

Publications DeepDose: a robust deep learning-based dose engine for abdominal tumours in a 1.5 T MRI radiotherapy system. Tsekas G, Bol GH, Raaymakers BW, Kontaxis C. Phys Med Biol. 2021 Feb 5. (paper)

Keywords: MRI-guided radiotherapy | dose calculations|deep learning for radiotherapy | secondary dose engine| AI | MR-Linac | QA |

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