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
this https URL

提出一种基于深度学习的后处理方法，通过关键点检测任务修复肺部管状结构的连接性问题，解决当前深度学习方法在肺部病理诊断中的实用性问题。新的肺部树修复数据集（PTR）包括 800 个完整的三维肺部结构模型，可公开获取。