Self-supervised 3D Skeleton Completion for Vascular Structures

3D skeleton is critical for analyzing vascular structures with many applications, it is however often limited by the broken skeletons due to image degradation. Existing methods usually correct such skeleton breaks via handcrafted connecting rules or rely on nontrivial manual annotation, which is susceptible to outliers or costly especially for 3D data. In this paper, we propose a self-supervised approach for vasculature reconnection. Specifically, we generate synthetic breaks from confident skeletons and use them to guide the learning of a 3D UNet-like skeleton completion network. To address serious imbalance among different types of skeleton breaks, we introduce three skeleton transformations that largely alleviate such imbalance in synthesized break samples. This allows our model to effectively handle challenging breaks such as bifurcations and tiny fragments. Additionally, to encourage the connectivity outcomes, we design a novel differentiable connectivity loss for further improvement. Experiments on a public medical segmentation benchmark and a 3D optical coherence Doppler tomography (ODT) dataset show the effectiveness of our method.