  Accurate segmentation of pulmonary airways and vessels is crucial for the
diagnosis and treatment of pulmonary diseases. However, current deep learning
approaches suffer from disconnectivity issues that hinder their clinical
usefulness. To address this challenge, we propose a post-processing approach
that leverages a data-driven method to repair the topology of disconnected
pulmonary tubular structures. Our approach formulates the problem as a keypoint
detection task, where a neural network is trained to predict keypoints that can
bridge disconnected components. We use a training data synthesis pipeline that
generates disconnected data from complete pulmonary structures. Moreover, the
new Pulmonary Tree Repairing (PTR) dataset is publicly available, which
comprises 800 complete 3D models of pulmonary airways, arteries, and veins, as
well as the synthetic disconnected data. Our code and data are available at
https://github.com/M3DV/pulmonary-tree-repairing.
