用他人手术视频标注初始化，实现零样本自动分割
One Patient's Annotation is Another One's Initialization: Towards Zero-Shot Surgical Video Segmentation with Cross-Patient Initialization
- 跨患者使用他人标注帧作为初始化跟踪起点
- 性能媲美甚至超过自身标注，准确率提升显著
- 适合追求零手动干预的智能手术系统研发者
视频目标分割是一项适用于实时手术视频分析的新兴技术，可在术中提供一致的帧追踪，助力临床决策。然而，其应用受限于需人工指定追踪目标，难以在手术场景中部署。本文提出创新方案：利用其他患者的已标注帧作为追踪起始帧。实验发现，该非传统方法可达到甚至超越患者自身标注帧的性能，实现更自主、高效的AI辅助手术流程。我们进一步分析该方法的优势与局限，揭示其在提升分割精度的同时降低人工输入的需求。研究结果为优化跨患者帧选择策略提供了关键洞见，为实时手术视频分析的后续研究奠定基础。
原文摘要 · Abstract (English)
Video object segmentation is an emerging technology that is well-suited for real-time surgical video segmentation, offering valuable clinical assistance in the operating room by ensuring consistent frame tracking. However, its adoption is limited by the need for manual intervention to select the tracked object, making it impractical in surgical settings. In this work, we tackle this challenge with an innovative solution: using previously annotated frames from other patients as the tracking frames. We find that this unconventional approach can match or even surpass the performance of using patients' own tracking frames, enabling more autonomous and efficient AI-assisted surgical workflows. Furthermore, we analyze the benefits and limitations of this approach, highlighting its potential to enhance segmentation accuracy while reducing the need for manual input. Our findings provide insights into key factors influencing performance, offering a foundation for future research on optimizing cross-patient frame selection for real-time surgical video analysis.
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