31.07.2026

Master's thesis, research internship or guided research (MA/SA/GR): CT-Based Virtual Donor–Recipient Liver Matching

CT-Based Virtual Donor–Recipient Liver Matching

Master’s Thesis, Clinical Application Project, Guided Research Project

Overview & Motivation

Motivation: Donor livers may be rejected because they are considered too large or too small for a recipient. Current matching methods rely mainly on body size and estimated liver volume, but do not capture the patient-specific 3D shape of the liver or the available space inside the abdomen.

Problem: A poorly fitting graft may compress nearby organs or blood vessels and make implantation or abdominal closure difficult. Conversely, anatomically suitable grafts may be rejected because conventional measurements suggest a mismatch.

Goal: This project will develop a CT-based virtual matching framework that creates 3D models of the donor liver and recipient anatomy, virtually places the graft inside the recipient, and measures how well it fits.

The proposed pipeline will produce:

1. A 3D model of the donor liver and relevant recipient anatomy.
2. A realistic virtual position of the donor liver.
3. Interpretable measurements describing potential fitting problems.

Your Tasks

• Literature Review: Review relevant work on donor–recipient matching, image segmentation, 3D registration, virtual organ placement, and surgical planning.
• Method Design: Develop a framework for patient-specific virtual liver graft placement and geometric compatibility assessment.
• Implementation: Apply the developed framework to real donor and recipient CT scans to simulate graft placement, visualize the predicted result, and quantify donor–recipient fit.
• Evaluation: Validate the predicted graft placement using postoperative CT scans and compare the proposed approach with conventional matching methods based on body weight and liver volume.

Requirements

• Must-have: Strong programming skills in Python. Good understanding of 3D geometry or computer vision. Interest in medical image analysis and interdisciplinary research.
• Nice-to-have: Experience with CT image analysis, image segmentation, registration, or numerical optimization. Familiarity with tools such as SimpleITK, VTK, VMTK, TotalSegmentator, 3D Slicer, or ImFusion is an advantage.

A medical background is not required. Anatomical and clinical guidance will be provided by the supervisory team.

What We Offer

• Clinical Impact: Work on a real problem in liver transplantation.
• Research Opportunity: Gain experience in medical imaging, 3D modelling, geometric optimization, and surgical planning.
• Mentorship: Close technical supervision from a computer science PhD researcher and regular input from liver-transplant specialists.
• Publication Potential: Successful results may contribute to a publication in medical image computing, computer-assisted intervention, or transplantation research.

The expected result is a reproducible research prototype demonstrating whether patient-specific 3D modelling can provide useful information beyond conventional donor–recipient size-matching methods. The long-term goal is to improve donor-organ utilization, reduce unsuccessful allocation attempts, support reduced-size or split-liver planning, and prevent complications caused by oversized grafts.

Related Works

1. Briceño J, Ciria R, de la Mata M. Donor–recipient matching in liver transplantation. Transplant International. 2013.
2. Fukazawa K, Nishida S. Size mismatch in liver transplantation. Journal of Hepato-Biliary-Pancreatic Sciences. 2016.
3. Park S, et al. Improved graft survival using three-dimensional printing of the intra-abdominal cavity to prevent large-for-size syndrome. Annals of Hepato-Biliary-Pancreatic Surgery. 2025.
4. Bojstedt J, et al. Preoperative simulation for pediatric kidney transplantation using CT imaging and 3D printing. Frontiers in Transplantation. 2026.

Interested?

Please send an email outlining your interest in the project, along with your latest transcript of records and CV, to: Cagatay.Alici@med.uni-muenchen.de.

Kontakt: Cagatay.Alici@med.uni-muenchen.de

29.07.2026

BA/SA/MA: Development of Acoustic Membranes for Dry-Coupled Ultrasound Detection in Non-Invasive Glucose Monitoring

read more

Kontakt: maximilian.gotsch@tum.de

24.07.2026

Masterarbeiten: Leukemia Intrinsic Resistance (Beginning October 2026 WiSe)

read more

Kontakt: nadia.el-khawanky@tum.de

15.07.2026

Master thesis or research internship (MA/SA): Interferometer stabilization for optical ultrasound detection in non-invasive glucose monitoring

read more

Kontakt: maximilian.gotsch@tum.de

14.07.2026

Human gaze tracking during visual search

MA: Cognitively-realistic Spatial Visual Search Behavior for Embodied AI

read more

Kontakt: andrey.rudenko@tum.de

10.07.2026

MA: Development and Evaluation of a Scalable Whole-Body Data Collection Framework for Vision-Language-Action Learning on Quadrupedal Mobile Manipulators

read more

Kontakt: yuan_avs.gao@tum.de

01.07.2026

Abschlussarbeiten, Bachelor- und Masterarbeiten

Sie suchen gerade eine Diplomarbeit, ein Thema für eine Bachelor oder Master Thesis? Dann sind Sie hier richtig. In diesem Bereich sind Abschlussarbeiten aus allen Fakultäten zu finden.
Beachten Sie auch den entsprechenden Stichwortindex.

Wenn Sie selbst eine Diplomarbeit ausschreiben wollen, lesen Sie bitte vorher unbedingt das 'Best Practice Manual Stellenanzeigen'.

Kontakt: zfp@ed.tum.de; kerstin.kracht@tuhh.de

23.06.2026

Production and Purification of membraneproteins using E. coli and Chromatography

In this master’s thesis, several membrane proteins will be recombinantly produced using Escherichia coli as an expression host. These membrane will then be tested by a project partner as potential vaccines.
read more

Kontakt: j.galbusera@tum.de

22.06.2026

Bachelor’s thesis: Effect of anaerobic fungal pre-treatment on biomethane potential

THESIS OBJECTIVES As part of the BMLEH (FNR)-funded project LCR-PILZE, the potential of anaerobic fungi to improve methane yield during biogas production is being investigated in a pilot-scale biogas plant. The main hypothesis to be tested is if biological pre-treatment with anaerobic fungi accelerates degradation of lignocellulose-rich agricultural fermentation residues resulting in a more efficient biogas production. For this, the biogas production of 4 selected anaerobic fungi strains is determined using anaerobic batch reactors. TASKS Determination of biomethane potential (BMP) of anaerobic fungi in batch tests Assessment of lignocellulose degradation efficiency Comparison of the effects of biological pre-treatment of lignocellulosic residues with chemical and physical pre-treatment methods Hands-on work at the biogas pilot plant in Dürnast REQUIREMENTS Enrolled Bachelor’s student in Biology, Molecular Biotechnology, Bioprocess Engineering or a related field Good theoretical background in microbiology Experience with microbiological laboratory methods, ideally anaerobic microbiology Interest in sustainable biogas production processes WHAT WE OFFER Opportunity to contribute to an interdisciplinary and sustainable research project Training in methods of (anaerobic) microbiology and chemical analytics Insights into technical scale biogas plant Interested? Send an e-mail including a letter of motivation and short CV (incl. relevant expertise/internships etc.) to: cristina.gonzalezrivero@lfl.bayern.de
read more

Kontakt: cristina.gonzalez-rivero@tum.de

11.06.2026

Master's Thesis: Adaptive Cooperative Perception for ETSI-Based Vehicle-to-Infrastructure Systems Using Covariance-Aware Movement Clustering

The Chair of Robotics, Artificial Intelligence, and Real-Time Systems offers a Master’s thesis focusing on adaptive cooperative perception for ETSI-based Vehicle-to-Infrastructure systems using covariance-aware movement clustering and track-to-track fusion. The thesis investigates how object information from CAM and CPM messages, including covariances, velocities, and object parameters, can improve multi-agent object association and global tracking in urban traffic scenarios.
read more

Kontakt: erik-leo.hass@tum.de