研究内镜视频标注误差传播，提出自适应专家介入策略
Understanding Annotation Error Propagation and Learning an Adaptive Policy for Expert Intervention in Barrett's Video Segmentation
- 基于提示类型分析误差传播机制，设计可学习的重提示框架
- 在私有数据集与SUN-SEG上实现更高时序一致性与准确率
- 适合需降低人工标注成本的医学图像分割场景
内镜视频的精确标注对巴雷特食管中不典型增生的诊断至关重要，但该任务耗时且困难，因病变区域不规则且边界模糊。半自动工具如SAM2可通过跨帧传播标注简化流程，但微小误差易累积导致精度下降，需专家复核修正。本文系统研究了掩码、框、点三类提示下误差传播特性，提出成本感知的可学习重提示（L2RP）框架，通过调节人机成本参数平衡标注效率与分割精度。在私有巴雷特不典型增生数据集及公开的SUN-SEG基准上，实验表明该方法显著提升时序一致性，并优于基线策略。
原文摘要 · Abstract (English)
Accurate annotation of endoscopic videos is essential yet time-consuming, particularly for challenging datasets such as dysplasia in Barrett's esophagus, where the affected regions are irregular and lack clear boundaries. Semi-automatic tools like Segment Anything Model 2 (SAM2) can ease this process by propagating annotations across frames, but small errors often accumulate and reduce accuracy, requiring expert review and correction. To address this, we systematically study how annotation errors propagate across different prompt types, namely masks, boxes, and points, and propose Learning-to-Re-Prompt (L2RP), a cost-aware framework that learns when and where to seek expert input. By tuning a human-cost parameter, our method balances annotation effort and segmentation accuracy. Experiments on a private Barrett's dysplasia dataset and the public SUN-SEG benchmark demonstrate improved temporal consistency and superior performance over baseline strategies.
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