A Fully Automated CT-Based Airway Segmentation Algorithm using Deep Learning and Topological Leakage Detection and Branch Augmentation Approaches

摘要

Quantitative CT-based characterization of bronchial morphology is widely used in chronic obstructive pulmonary disease(COPD) related research and clinical studies. There are no fully automated airway tree segmentation methods, which iscritical for large multi-site COPD studies. A critical challenge is that airway segmentation failures, e.g., leakages or earlytruncation, in even a small fraction of cases warrants manual intervention for all cases. In this paper, we present a fullyautomatedCT-based hybrid algorithm for human airway segmentation that combines both deep learning and conventionalimage processing approaches. A three-dimensional (3-D) U-Net is developed to compute a voxel-level likelihood map ofairway lumen space from a chest CT image at total lung capacity (TLC). This likelihood map is fed into a conventionalimage processing cascade that iteratively augments airway branches and removes leakages using newly developed freezeand-grow and progressive threshold parameter relaxation approaches. The new method has been applied on fifteen TLChuman chest CT scans from an ongoing COPD Study and its performance has been quantitatively compared with theresults of a semi-automated industry-standard software involving manual review and correction. Experimental results showsignificant improvements in terms of branch level accuracy using the new method as compared to the unedited resultsfrom the industry-standard method, while matching with their manually edited results. In terms of segmentation volumeleakage, the new method significantly reduced segmentation leakages as compared to both unedited and edited results ofthe industry-standard method.