Interactive Angiography Image Segmentation

Angiography image segmentation is critical for cardiovascular disease diagnosis but faces challenges due to complex vascular structures and costly manual annotation. While deep learning offers solutions, supervised methods require large labeled datasets. Interactive segmentation (IIS) methods, using clicks or scribbles, provide a promising alternative by balancing efficiency and accuracy. However, the performance of existing IIS algorithms on angiography images remains underexplored. This study evaluates representative IIS algorithms on multiple angiography datasets, analyzing their performance characteristics and highlighting domain-specific challenges such as vascular complexity and imaging noise.