Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (11): 11-20.DOI: 10.3778/j.issn.1002-8331.2101-0128

Previous Articles     Next Articles

Review of Computer-Aided Diagnosis of Bronchiectasis with CT Images

WANG Liuyi, SONG Wen’ai, LIN Xinshan, YUE Ning, YANG Jijiang, WANG Qing, LEI Yi   

  1. 1.College of Software, North University of China, Taiyuan 030051, China
    2.Graduate School, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100730, China
    3.Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing 100029, China
    4.Department of Medical Imaging, Second Hospital of Shandong University, Jinan 250033, China
    5.Department of Automation, Tsinghua University, Beijing 100089, China
  • Online:2021-06-01 Published:2021-05-31



  1. 1.中北大学 软件学院,太原 030051
    2.中国医学科学院 北京协和医学院 研究生院,北京 100730
    3.中日友好医院 呼吸中心呼吸与危重症医学科,北京 100029
    4.山东大学第二医院 影像科,济南 250033
    5.清华大学 自动化系,北京 100089


Bronchiectasis is a common chronic respiratory disease, which seriously affects the quality of life of patients and brings heavy social and economic burden. Moreover, that diagnosis of bronchiectasis requires a certain degree of experience and professionalism on the part of the doctor in order to effectively obtain the correct result. With the development of artificial intelligence, target detection technology in the field of computer vision can be used to assist in the diagnosis of such diseases. This paper reports the research status of artificial intelligence diagnosis system for bronchiectasis, introduces the clinical diagnosis methods of bronchiectasis, and puts forward the technical route of computer aided diagnosis of this kind of disease based on it, summarizes the traditional and deep learning methods of CT image noise suppression, lung parenchyma extraction and lung lobe segmentation, aiming at the problem of lack of gold standard data set for bronchiectasis, it summarizes the problems and challenges of target detection applied to computer aided diagnosis from two aspects, and compares the characteristics and application scenarios of different algorithms in detail. Finally, the possible development trend in the future is discussed.

Key words: bronchiectasis, lung parenchyma segmentation, pulmonary lobectomy, object detection



关键词: 支气管扩张, 肺实质提取, 肺叶分割, 目标检测