Uncertainty-Aware Stenosis Segmentation in Digital Subtraction Angiography using Probabilistic Deep Learning
Overview
- Stenosis segmentation is essential for optimal stent choice.
- Expert opinions vary depending on experience, education, and skill level.
- Probabilistic segmentation can support quantifying uncertainty in stenosis segmentation.
- Relevant methods include Probabilistic U-Net and follow-up work on probabilistic segmentation ( arXiv:1907.01949 ).
- Quantify expert label uncertainty based on multi-expert labeled data.
Skills
- Python programming experience with Keras or PyTorch, scikit-learn, pandas, and NumPy
- Deep learning knowledge, especially CNNs and U-Net
- 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