Tracking and segmentation of the airways in chest CT using a fully convolutional network

Abstract

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes such as lung cancer detection, chronic obstructive pulmonary disease (COPD), and surgical navigation. However, due to the complex tree-like structure of the airways, obtaining segmentation results with high accuracy for a complete 3D airway extraction remains a challenging task. In recent years, deep learning based methods, especially fully convolutional networks (FCN), have improved the state-of-the-art in many segmentation tasks. 3D U-Net is an example that optimized for 3D biomedical imaging. It consists of a contracting encoder part to analyze the input volume and a successive decoder part to generate integrated 3D segmentation results. While 3D U-Net can be trained for any 3D segmentation task, its direct application to airway segmentation is challenging due to differently sized airway branches. In this work, we combine 3D deep learning with image-based tracking in order to automatically extract the airways. Our method is driven by adaptive cuboidal volume of interest (VOI) analysis using a 3D U-Net model. We track the airways along their centerlines and set VOIs according to the diameter and running direction of each airway. After setting a VOI, the 3D U-Net is utilized to extract the airway region inside the VOI. All extracted candidate airway regions are unified to form an integrated airway tree. We trained on 30 cases and tested our method on an additional 20 cases. Compared with other state-of-the-art airway tracking and segmentation methods, our method can increase the detection rate by 5.6 while decreasing the false positives (FP) by 0.7 percentage points.