Surgical Video Segmentation

Surgical video segmentation, the automated identification and delineation of objects within surgical videos, aims to improve surgical precision and efficiency. Current research focuses on adapting and optimizing general-purpose segmentation models like SAM2 for the unique challenges of surgical video, including real-time processing and handling of noisy or complex scenes, often employing techniques like efficient frame pruning and contrastive learning. Improved segmentation accuracy and speed are key goals, with advancements impacting computer-assisted surgery, training, and the development of standardized clinical reporting tools. Federated learning approaches are also being explored to address data privacy concerns and enable collaboration across institutions.