用局部匹配与时空一致性提升X光造影视频少样本分割精度
Few-Shot Video Object Segmentation in X-Ray Angiography Using Local Matching and Spatio-Temporal Consistency Loss
- 通过方向采样实现动态局部匹配，避免依赖特定硬件的复杂计算
- 在三个数据集上超越现有方法，对新类别也表现优异
- 首次公开多目标X光造影视频分割数据集，适合医疗视觉研究者
我们提出一种新型少样本视频对象分割（FSVOS）模型，采用局部匹配策略将搜索范围限制在最相关的邻近像素。不同于依赖低效标准im2col实现（如空间卷积、深度可分离卷积和特征偏移机制）或硬件特定CUDA内核（如可变形注意力和邻域注意力）的方法，这些方法常因跨非CUDA设备移植性差而受限，我们通过基于方向的采样视角重新组织局部采样过程。具体地，实现了一种非参数化采样机制，支持动态变化的采样区域，无需参数层计算开销且无需重新训练即可适应多样的空间结构。为进一步增强帧间特征一致性，设计了监督式时空对比学习方案，强制特征表示保持一致。此外，引入首个公开的多对象分割在X射线造影视频数据集（MOSXAV），包含精细的人工标注分割真值。在CADICA、XACV和MOSXAV数据集上的大量实验表明，所提方法在分割准确率和泛化能力（即已见与未见类别）方面均优于当前最先进方法。本工作为临床应用提供了更高灵活性与潜力。代码已开源。
原文摘要 · Abstract (English)
We introduce a novel FSVOS model that employs a local matching strategy to restrict the search space to the most relevant neighboring pixels. Rather than relying on inefficient standard im2col-like implementations (e.g., spatial convolutions, depthwise convolutions and feature-shifting mechanisms) or hardware-specific CUDA kernels (e.g., deformable and neighborhood attention), which often suffer from limited portability across non-CUDA devices, we reorganize the local sampling process through a direction-based sampling perspective. Specifically, we implement a non-parametric sampling mechanism that enables dynamically varying sampling regions. This approach provides the flexibility to adapt to diverse spatial structures without the computational costs of parametric layers and the need for model retraining. To further enhance feature coherence across frames, we design a supervised spatio-temporal contrastive learning scheme that enforces consistency in feature representations. In addition, we introduce a publicly available benchmark dataset for multi-object segmentation in X-ray angiography videos (MOSXAV), featuring detailed, manually labeled segmentation ground truth. Extensive experiments on the CADICA, XACV, and MOSXAV datasets show that our proposed FSVOS method outperforms current state-of-the-art video segmentation methods in terms of segmentation accuracy and generalization capability (i.e., seen and unseen categories). This work offers enhanced flexibility and potential for a wide range of clinical applications. Code is available at https://github.com/xilin-x/XRAVOS
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