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[2602.21855] Understanding Annotation Error Propagation and Learning an Adaptive Policy for Expert Intervention in Barrett's Video Segmentation
Summary
This article explores the challenges of annotation error propagation in endoscopic video segmentation, proposing a framework for optimizing expert intervention to enhance accuracy.
Why It Matters
Accurate video annotation is critical in medical imaging, particularly for detecting dysplasia in Barrett's esophagus. This study addresses the inefficiencies in current methods, potentially improving diagnostic accuracy and reducing expert workload through an adaptive intervention policy.
Key Takeaways
- Annotation errors in video segmentation can propagate, affecting accuracy.
- The proposed Learning-to-Re-Prompt (L2RP) framework optimizes when to seek expert intervention.
- Tuning a human-cost parameter helps balance annotation effort and accuracy.
- Experiments show improved performance over existing methods on benchmark datasets.
- This approach could streamline workflows in medical video analysis.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.21855 (cs) [Submitted on 25 Feb 2026] Title:Understanding Annotation Error Propagation and Learning an Adaptive Policy for Expert Intervention in Barrett's Video Segmentation Authors:Lokesha Rasanjalee, Jin Lin Tan, Dileepa Pitawela, Rajvinder Singh, Hsiang-Ting Chen View a PDF of the paper titled Understanding Annotation Error Propagation and Learning an Adaptive Policy for Expert Intervention in Barrett's Video Segmentation, by Lokesha Rasanjalee and 4 other authors View PDF HTML (experimental) Abstract: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...