Cross-Domain Translation of Angiography: From Stenosis to Healthy Vessel Appearance
Overview
- Optimal stent choice is essential for optimal long-term therapy outcomes.
- Knowing the original size and shape of a diseased vessel can support the optimal stent choice.
- Develop an unsupervised learning framework for data-domain transfer from diseased to healthy vessels, for example with a CycleGAN.
- Work with coronary and/or iliac angiography data.
Skills
- Python programming experience with Keras or PyTorch, scikit-learn, pandas, and NumPy
- Deep learning knowledge, especially CNNs and GANs
- Image processing knowledge
- Basic medical or anatomy knowledge is a plus
Tasks
- Literature research
- Data preparation and preprocessing pipeline implementation
- Probabilistic segmentation and/or detection model implementation, training, and evaluation
Contact
Daniel Wulff