Diploma thesis: Model-based Segmentation of Abdominal Aortic Stent Grafts in 2D and 3D Interventional Images

Advisor: Nassir Navab
Supervision by: Stefanie Demirci, Max Baust

Abstract

By now, abdominal aortic stenting has become a well-established medical procedure. Still, it is a very cumbersome method for the surgeon – both in an intra- and post-interventional sense – that requires quite an amount of manual interaction. Thus, there is a lot of room for improvement using computer-aided techniques. During the operation, modern navigation methods can support the surgeon while reducing the radiation dose for the patient. Standards have to be developed in order to support the surgeon in defining and correctly placing the stent graft and evaluate the quality of the stent placement.

Automatic detection of the stent graft in interventional 2D and 3D image data is the essential basis to achieve these goals and, thus, constitutes the main concern of this diploma thesis. The use of model-based rigid and deformable registration techniques for stent graft extraction from medical images is evaluated along with the necessary preprocessing steps to find a proper initialization for the registration algorithm. The latter includes dominant local direction analysis using steerable filters as well as the use of distance maps to improve the registration results. The results are evaluated using synthetic test data and real patient data.

Tasks

• Review of possible approaches for virtual stenting applications
• Review of different extraction techniques for stent grafts
• Implementation of virtual stenting application
• Validation of method on synthetic test data and real patient data in collaboration with Dr. Ghotbi (Kreisklinik München-Pasing)

Requirements

• A good knowledge of C++ is mandatory.
• Basic knowledge of prototyping using MATLAB
• Basic knowledge of image processing methods is required.
• An adequate education and interest in mathematics is required.
• Interest in medical applications is required and knowledge can be acquired during DA.