Skelite: Compact Neural Networks for Efficient Iterative Skeletonization

Luis D. Reyes Vargas, Martin J. Menten, Johannes C. Paetzold, Nassir Navab, Mohammad Farid Azampour

Abstract: 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.