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

31.07.2026, Abschlussarbeiten, Bachelor- und Masterarbeiten

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