Synergistic Bleeding Region and Point Detection in Laparoscopic Surgical Videos

Abstract: Intraoperative bleeding in laparoscopic surgery causes rapid obscuration of the operative field to hinder the surgical process and increases the risk of postoperative complications. Intelligent detection of bleeding areas can quantify the blood loss to assist decision-making, while locating bleeding points helps surgeons quickly identify the source of bleeding and achieve hemostasis in time to improve surgical success rates. In this study, we first construct a real-world laparoscopic surgical bleeding detection dataset, named SurgBlood, comprising 5,330 frames from 95 surgical video clips with bleeding region and point annotations. Accordingly, we develop a dual-task synergistic online detector called BlooDet, designed to perform simultaneous detection of bleeding regions and points in laparoscopic surgery. Our framework embraces a dual-branch bidirectional guidance design based on Segment Anything Model 2 (SAM 2). The mask branch detects bleeding regions through adaptive edge and point prompt embeddings, and the point branch leverages mask memory to induce bleeding point memory modeling and capture the direction of bleed point movement via inter-frame optical flow. By bidirectional guidance, the two branches explore potential spatial-temporal relationships while leveraging memory modeling to infer the current bleeding condition. Extensive experiments demonstrate that our baseline outperforms 12 counterparts on SurgBlood in both bleeding region and point detection.