Reconstructing missing anatomical structures is a significant challenge in surgery, particularly in areas such as cranial implant design and cranioplasty. When parts of the skull are damaged or removed, for example, after trauma, surgery, or tumor resection, accurate and patient-specific reconstruction is essential for protective, functional, and aesthetic reasons.
With this thesis, you will investigate novel approaches to reconstructing missing anatomical structures based on medical image data. You will explore how advanced 3D techniques, such as Gaussian Splatting or generative modeling, can be utilized to create accurately fitting, personalized shapes. The direction of the project can be tailored to your interests, whether in machine learning, geometric modeling, or innovative 3D representations. The overarching goal is to bridge powerful 3D techniques with real-world challenges in personalized medicine, improving the detail, coherence, and clinical relevance of reconstructed pathological anatomical structures.
Your Profile
• Enrolled in a Master’s program in Computer Science, Applied Mathematics, or a related field
• Strong interest and sufficient background in machine learning, 3D computer vision, graphics, and shape modeling
• Proficient in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
• Knowledge of 3D data, Gaussian Splatting, Neural Radiance Fields (NeRF), or implicit shape representations is a plus
What We Offer
• Work with state-of-the-art AI methods in medical imaging
• Close mentorship and collaboration with experienced researchers
• Access to medical datasets and high-performance computing infrastructure at ZIB
• Opportunity to contribute to high-impact publications
This Master's thesis is independent of any specific university. If you are interested in this topic, find a professor who supports you and let our researchers at ZIB supervise you with passion and devotion.
How to apply
Apply to Dr. Jianning Li and/or Dr. Stefan Zachow, including supporting documents, such as a cover letter, your CV, enrollment confirmation, and transcripts.
