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Vienna, Austria

ESTRO 2023

Session

Saturday
May 13
16:45 - 17:45
Business Suite 3-4
Automation and machine learning
Dietmar Georg, Austria
Poster Discussions are presented in one of the sessions scheduled at the two poster discussion theatres. Each author will present a digital poster orally for 2 minutes, followed by 2 minutes for discussion. Sessions will not be recorded.
Poster Discussion
Physics
Automated MR-Only RT for H&N cancer: synthetic CTs, autocontouring and automated treatment planning
Iris Lauwers, The Netherlands
PD-0324
unedited deep-learning based OARs are suitable for rigorous head and neck treatment planning
Vladimir Feygelman, USA
PD-0325
Alternative DL segmentation approaches for clinical partially-labeled HN data: transformers or Unet?
Lucia Cubero, Spain
PD-0326
Clinical benefits of multi-modality gross tumor volume auto-delineation in head and neck cancer
Heleen Bollen, Belgium
PD-0327
Evaluation of the clinical use of a deep learning model for breast cancer treatment plan generation
Nienke Bakx, The Netherlands
PD-0328
Evaluation of a CT scanner based deep learning auto-contouring solution for prostate radiotherapy
Matthew Williams, United Kingdom
PD-0329
Clinical generalisability of a custom auto-contouring model for Prostate radiotherapy
Marina Khan, United Kingdom
PD-0330
Automated dynamic trajectory radiotherapy planning using 4pi-IMRT path-finding - proof of concept
Hannes Anton Loebner, Switzerland
PD-0331
Validation of relative beam spot position in a linear accelerator using a simple phantom
Hans Lynggaard Riis, Denmark
PD-0332
Interpreting generalizability of automatic GTV segmentation in locally advanced cervical cancer
Rahimeh Rouhi, France
PD-0333
Techniques to optimize auto-segmentation of small OARs in pediatric patients undergoing CSI
James Tsui, Canada
PD-0334
A comparison between 2D and 3D GAN as a supporting tool for rectum segmentation on 0.35 T MR images
Marica Vagni, Italy
PD-0335