Skelite: Compact Neural Networks for Efficient Iterative Skeletonization
This work addresses the problem of efficient and accurate skeletonization for medical tasks like vessel segmentation, offering a significant speed improvement with broad applicability, though it is incremental as it builds on existing iterative methods with neural enhancements.
The paper tackled the trade-off between computational efficiency and connectivity in skeletonization algorithms by proposing a learnable framework that trains compact neural networks for iterative skeletonization, achieving a 100 times speedup over topology-constrained methods while maintaining high accuracy and generalization to new domains.
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