用少量视频标注+大量图像数据，实现医疗视频目标分割
Reducing Annotation Burden: Exploiting Image Knowledge for Few-Shot Medical Video Object Segmentation via Spatiotemporal Consistency Relearning
- 用已标注图像训练模型，再通过时序一致性重学习提升视频分割性能
- 仅需少量视频帧标注，在医学视频上达到领先效果
- 适合标注稀缺的医疗视频场景，尤其适用于图像数据丰富但视频标注少的领域
少样本视频目标分割旨在降低标注成本，但现有方法仍需大量密集帧标注，而医学领域此类标注极为稀缺。本文研究一种极低数据条件下的分割方法：仅使用少数视频帧的标注，并利用大量已标注图像来减少昂贵的视频标注需求。提出两阶段框架：首先基于标注图像训练少样本分割模型；随后在医学视频上引入时空一致性重学习，强制相邻帧间保持一致性，并在特征与预测层面约束图像模型与重学习模型的一致性。实验表明，该方法优于当前最优少样本分割方法。模型有效弥合了医学图像与稀疏标注视频之间的差距，在极低数据条件下实现优异的视频分割性能。代码已开源：https://github.com/MedAITech/RAB。
原文摘要 · Abstract (English)
Few-shot video object segmentation aims to reduce annotation costs; however, existing methods still require abundant dense frame annotations for training, which are scarce in the medical domain. We investigate an extremely low-data regime that utilizes annotations from only a few video frames and leverages existing labeled images to minimize costly video annotations. Specifically, we propose a two-phase framework. First, we learn a few-shot segmentation model using labeled images. Subsequently, to improve performance without full supervision, we introduce a spatiotemporal consistency relearning approach on medical videos that enforces consistency between consecutive frames. Constraints are also enforced between the image model and relearning model at both feature and prediction levels. Experiments demonstrate the superiority of our approach over state-of-the-art few-shot segmentation methods. Our model bridges the gap between abundant annotated medical images and scarce, sparsely labeled medical videos to achieve strong video segmentation performance in this low data regime. Code is available at https://github.com/MedAITech/RAB.
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