肺部CT图像气管树分割技术研究进展

肺部气管是人体与外界进行气体交换的唯一通路;其解剖结构信息可用于诊断呼吸系统疾病。计算机断层扫描技术(CT)是检测呼吸系统疾病的主要手段,但因就诊人数多、图像数据量大等因素;导致人工阅片费时费力。而肺部气管树的自动提取与分割;是实现自动化定量分析与呼吸系统疾病辅助诊断的前提。首先对肺部气管树分割技术的背景及意义进行介绍;然后分析对比传统分割技术、基于管状结构检测的分割技术以及基于机器学习的分割技术所运用的研究方法和存在的问题。最后指出提高肺部气管树分割效果;依赖于将气管分割技术与泄漏剔除技术相互结合;需要在尽可能分割出多数气管树分枝的基础上;消除分割结果中存在的伪气管区域。

A Review of Segmentation of Pulmonary Airway in Lung CT Scans

Pulmonary airway is the only access between the human body and the external environment, therefore the anatomy information of pulmonary airway is helpful for diagnosis of respiratory system disease. Computed tomography (CT) is one of the main methods for respiratory disease diagnosis. However, due to the large amounts of patients and images, manual reading of CT images is tedious and time-consuming. The automatic segmentation and extraction of pulmonary airway tree is the precondition of automatic analysis and computer-assisted diagnosis. Hence, according to the research progress of segmentation of pulmonary airway in recent years, we first introduced the background and the meaning of the airway segmentation. Then we analyzed the traditional methods, the method based on tube structure detection and machine learning, and the problem they met. Finally, we proposed that integrating the step of segmentation and leakage limitation can improve the accuracy and the number of branch, which means segmenting as many airways as possible at first and then eliminating the leakage.