用模拟错误训练模型，自动修正椎骨关键点识别中的典型错误。
Bones Can't Be Triangles: Accurate and Efficient Vertebrae Keypoint Estimation through Collaborative Error Revision
- 通过分析典型错误类型并用仿真数据训练，实现自动纠错。
- 在三个公开数据集上显著优于现有方法，降低人工修正负担。
- 适合需要高精度椎骨定位的医学影像分析场景。
近期交互式关键点估计方法虽提升了精度并减少用户干预，但在椎骨关键点估计中，因错误点密集或重叠，依赖用户修正成本较高。本文提出新方法 KeyBot，专门识别并修正模型中的显著与典型错误，类比于人工修正。通过刻画典型错误模式并使用模拟错误数据训练，KeyBot 能有效纠正错误，大幅减少用户工作量。在三个公开数据集上的定量与定性评估表明，KeyBot 显著优于现有方法，在交互式椎骨关键点估计中达到最新技术水平。源代码与演示视频见：https://ts-kim.github.io/KeyBot/
原文摘要 · Abstract (English)
Recent advances in interactive keypoint estimation methods have enhanced accuracy while minimizing user intervention. However, these methods require user input for error correction, which can be costly in vertebrae keypoint estimation where inaccurate keypoints are densely clustered or overlap. We introduce a novel approach, KeyBot, specifically designed to identify and correct significant and typical errors in existing models, akin to user revision. By characterizing typical error types and using simulated errors for training, KeyBot effectively corrects these errors and significantly reduces user workload. Comprehensive quantitative and qualitative evaluations on three public datasets confirm that KeyBot significantly outperforms existing methods, achieving state-of-the-art performance in interactive vertebrae keypoint estimation. The source code and demo video are available at: https://ts-kim.github.io/KeyBot/
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