Postdoctoral positions:
- None available at the moment.
PhD positions:
- PhD Candidate Prompt-based AI for Radiotherapy Target Volume Segmentation
Accurate delineation of tumor target volumes is a critical step in the radiotherapy workflow. While deep learning models have successfully enabled automated segmentation of organs-at-risk, the delineation of tumor targets remains a major challenge due to large interpatient variability. Moreover, in contrast to healthy organs, delineation of tumors requires integration of clinical context such as tumor stage, surgery reports, and patient-specific risk profiles. Recent work has shown that large language model (LLM)-driven prompt-based segmentation can outperform vision-only approaches.
In this PhD project, you will develop and clinically translate next-generation multimodal AI models for prompt-driven segmentation, using clinical text and 3D imaging data in tandem. The goal is to move beyond static image-to-image auto-contouring and instead create dynamic, context-aware systems that adapt to each patient’s scenario.
PROVE Supervisors: Lois Daamen, Guus Grimbergen
Master student projects:
- None available at the moment.
Bachelor student projects:
- None available at the moment.