用可学习的轻量网络实现快速且连通的骨骼化，速度提升100倍
Skelite: Compact Neural Networks for Efficient Iterative Skeletonization
- 设计可微迭代骨架化框架，结合合成数据与模型蒸馏训练紧凑网络
- 在2D/3D任务中实现100倍加速，精度媲美传统拓扑约束方法
- 无需微调即可跨域泛化，适合医疗图像中血管等结构的高效处理
骨骼化从图像中提取简洁的几何与拓扑表征，对保留曲线结构连通性至关重要，广泛应用于医学图像分割任务。现有方法存在显著权衡：基于形态学的方法计算高效但易断裂，而拓扑保持方法虽准确却耗时巨大。本文提出一种新型训练框架，通过可学习组件实现迭代式骨骼化算法。该框架利用合成数据、任务特定增强和模型蒸馏策略，训练出能生成细长且连通骨架的紧凑神经网络，并采用完全可微的迭代算法。实验表明，该方法相较拓扑约束算法提速100倍，同时保持高精度并可在新域上有效泛化，无需微调。2D与3D基准测试及下游验证均证明其计算效率与实际应用价值。
原文摘要 · Abstract (English)
Skeletonization extracts thin representations from images that compactly encode their geometry and topology. These representations have become an important topological prior for preserving connectivity in curvilinear structures, aiding medical tasks like vessel segmentation. Existing compatible skeletonization algorithms face significant trade-offs: morphology-based approaches are computationally efficient but prone to frequent breakages, while topology-preserving methods require substantial computational resources. We propose a novel framework for training iterative skeletonization algorithms with a learnable component. The framework leverages synthetic data, task-specific augmentation, and a model distillation strategy to learn compact neural networks that produce thin, connected skeletons with a fully differentiable iterative algorithm. Our method demonstrates a 100 times speedup over topology-constrained algorithms while maintaining high accuracy and generalizing effectively to new domains without fine-tuning. Benchmarking and downstream validation in 2D and 3D tasks demonstrate its computational efficiency and real-world applicability
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