Airway Segmentation from 3D Chest CT Volumes Based on Volume of Interest Using Gradient Vector Flow

Abstract

Some lung diseases are related to bronchial airway structures and morphology. Although airway segmentation from chest CT volumes is an important task in the computer-aided diagnosis and surgery assistance systems for the chest, complete 3-D airway structure segmentation is a quite challenging task due to its complex tree-like structure. In this paper, we propose a new airway segmentation method from 3D chest CT volumes based on volume of interests (VOI) using gradient vector flow (GVF). This method segments the bronchial regions by applying the cavity enhancement filter (CEF) to trace the bronchial tree structure from the trachea. It uses the CEF in the VOI to segment each branch. And a tube-likeness function based on GVF and the GVF magnitude map in each VOI are utilized to assist predicting the positions and directions of child branches. By calculating the tube-likeness function based on GVF and the GVF magnitude map, the airway-like candidate structures are identified and their centrelines are extracted. Based on the extracted centrelines, we can detect the branch points of the bifurcations and directions of the airway branches in the next level. At the same time, a leakage detection is performed to avoid the leakage by analysing the pixel information and the shape information of airway candidate regions extracted in the VOI. Finally, we unify all of the extracted bronchial regions to form an integrated airway tree. Preliminary experiments using four cases of chest CT volumes demonstrated that the proposed method can extract more bronchial branches in comparison with other methods.