Cara Lab algorithms
At CARA Lab, we focus on developing advanced AI-driven methods for intracoronary imaging analysis. Our research aims to improve segmentation, artifact detection, and the identification of rare structures such as thrombus or plaque rupture. Additionally, we work on models for stent detection and characterization. Currently, our published and available models include segmentation and artifact detection algorithms.
Segmentation
Our segmentation model, OCT-AID, is led by Ruben van der Waerden and is designed to precisely delineate key structures within intracoronary OCT images, aiding in the identification of vessel boundaries, plaque types, and other relevant features. By leveraging deep learning techniques, our model enhances automated interpretation and assists clinicians in decision-making.
Artifact Detection
Our artifact detection algorithm, led by Pierandrea Cancian focuses on identifying attenuation artifacts within OCT recordings. It employs an A-line-based approach to distinguish between valid imaging data and regions affected by signal loss due to blood and gas bubbles, ensuring more accurate assessments of vessel structures and pathology.
Stay tuned for updates on our ongoing developments in thrombus detection, plaque rupture analysis, and stent modeling.