Refining Airway Segmentation Through Breakage Filling and Leakage Reduction Using Point Clouds

Bronchoscopy reveals air passages and internal tissues for accurate diagnosis of various lung diseases. Robot-assisted bronchoscopy using an airway tree model can help path planning before surgery and navigation during surgery. In airway tree modeling, though volumetric deep learning methods have achieved good performance for airway segmentation, it remains a challenge due to the breakages and leakages. Some existing methods adopt post-processing using traditional methods like morphological and fuzzy connected algorithms. Also, some methods convert the volumetric data to point cloud format to refine segmentation. In this paper, we develop a new point cloud-based approach to refine volumetric segmentation. To address the breakage issue, we approach it as a regression problem of the branch extension direction and length. To tackle the leakage issue, we approach it as a segmentation task to eliminate leakages caused by breakage filling and from volumetric segmentation. Moreover, the direction information of branches is crucial for constructing the airway tree while point clouds do not naturally encode it. To introduce this information, we propose a directional feature aggregation, which first decomposes features of neighboring points based on their locations and aggregates decomposed features to aid the network in capturing the directional information effectively. Our proposed model has been evaluated on two public datasets, and the results show that our refinement can improve the volumetric segmentation.