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Accurate identification of anatomical structures is essential for safe congenital cardiac surgery and for the development of assistive robotic systems. However, scalable annotation of open-heart surgical video remains a major challenge due to dynamic tissue motion, occlusion, and anatomical variability. The aim of this study was to develop and evaluate a human-in-the-loop segmentation and tracking
