Master Thesis: Brain Tumor Radiotherapy Planning using state-of-the-art Computer Vision

03.07.2025, Diplomarbeiten, Bachelor- und Masterarbeiten

Are you passionate about AI for healthcare? Join us for a master’s thesis project at the intersection of medical imaging and machine learning.

Are you passionate about AI for healthcare? Join us for a master’s thesis project at the intersection of medical imaging and machine learning.

The Project:
Gliomas are among the most aggressive brain tumors, and radiotherapy is a key part of treatment. Traditional planning relies on pure distance to resection-based methods. Recently, biophysical modeling [1] and numerical optimization [2] have been developed for patient-specific treatment. With recent access to larger clinical MRI datasets, we now want to explore whether state-of-the-art deep learning methods can predict optimal radiotherapy plans directly from 3D MRI.

Your Role:
You will develop and evaluate machine learning models (e.g., UNets or transformer-based architectures) to propose radiotherapy plans based on 3D magnetic resonance images.

Your qualifications:
• Motivated by developing innovative AI solutions with real-world clinical impact.
• Currently enrolled in computer science, mathematics, physics, biomedical engineering, or a related discipline.
• Strong Python programming skills, preferably experienced with frameworks like PyTorch
• Previous experience with 3D medical imaging is beneficial but not mandatory.

What we offer:
• The opportunity to contribute to cutting-edge research at the intersection of AI and medical imaging.
• Close mentorship and access to advanced computational resources and hardware.
• Collaborative environment with interdisciplinary experts in artificial intelligence, medical imaging, and computational science.

How to apply: Send your CV and transcript via email to j.weidner@tum.de and lucas.zimmer@tum.de . Supervision will be by Prof. Benedikt Wiestler.

[1] Balcerak, Michal, et al. "Individualizing glioma radiotherapy planning by optimization of data and physics-informed discrete loss." arXiv preprint arXiv:2312.05063 (2023).
[2] Weidner, Jonas, et al. "Spatial Brain Tumor Concentration Estimation for Individualized Radiotherapy Planning." arXiv preprint arXiv:2412.13811 (2024).

Kontakt: j.weidner@tum.de and lucas.zimmer@tum.de