Projects Last Projects
Coronary angiography video segmentation method for assisting cardiovascular disease interventional treatment
Natalie Mendelson and Daniel Katz
Supervised by Noam Rotstein
Percutaneous coronary intervention (PCI) is a procedure to diagnose and treat coronary artery disease but carries risks like artery dissection and perforation. The CathAlert project aims to improve an AI-based alert system to detect and warn against mispositioning of coronary catheters and wires in real-time, enhancing patient safety.
Methods: The system was developed using a large dataset of hazard-annotated frames and segmentation masks. Various training strategies and architectural modifications were implemented to improve model performance, including residual connections and temporal data processing.
Results: The enhanced system showed significant improvements in detecting key structures and potential risks, with success rates of 70-80% in segmentation tasks and ROC AUC scores of 0.71 to 0.88 for hazard alerts. Data augmentations increased robustness, though temporal data did not significantly enhance performance.
Conclusions: The research shows potential for accurate hazard identification during PCI. Future work includes further integration of temporal data and training specialized decoders to improve performance, aiming to enhance patient safety and procedural outcomes in the Cath-Lab.
Please, see project report.
Please, see final presentation.