Description
Deep Learning methods can be used to reduce the severity of Metal Artefacts in Cone-Beam CT images. This thesis aims to design and validate a simulation pipeline, which creates realistic X-Ray projection images from available CT volumes and metal object meshes. Additionally, 2D and 3D ground truth binary masks should provide a segmentation of metal to be used as ground truth during training. The explicit focus of the data generation will be placed on the accuracy of the Metal Artefacts.
Your qualifications
- Fluent in Python and/or C++
- Knowledge of Homogenous Coordinates and Projective Mapping
- Interest in Quality Software Development / Project Organisation
- Experience with CUDA and interface to C++ / Python (optional, big plus)
You will learn
- to organize a short-term project (report status and structured sub-goals)
- to scientifically evaluate the developed methods
- to report scientific findings in a thesis / a publication
The thesis is funded by Siemens Healthineers and can be combined with a working student position prior to or after the thesis (up to 12 h/week). If interested, please write a short motivational email to Maxi.Rohleder@fau.de highlighting your qualifications and describe one related code project you are proud of. Please also attach your CV and transcript of records from your current and previous studies.