摘要
支气管扩张症是一种常见的慢性呼吸道疾病,严重影响患者的生活质量,带来了沉重的社会经济负担。随着人工智能的发展,可利用计算机视觉领域的目标检测技术辅助诊断这类疾病。报告了支气管扩张症人工智能诊断系统的研究现状,介绍了支气管扩张症的临床诊断方式,并基于此提出了计算机辅助诊断该类疾病的诊断技术路线,总结了CT影像噪声抑制、肺实质提取、肺叶分割的传统和深度学习方法,针对支气管扩张金标准数据集匮乏的问题,从两个方面综述了目标检测应用于计算机辅助诊断的问题及挑战,详细比较了不同算法的特点和适用场景。最后讨论了未来可能的发展趋势。
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.
作者
王六一
宋文爱
林鑫山
岳宁
杨吉江
王青
雷毅
WANG Liuyi;SONG Wen’ai;LIN Xinshan;YUE Ning;YANG Jijiang;WANG Qing;LEI Yi(College of Software,North University of China,Taiyuan 030051,China;Graduate School,Chinese Academy of Medical Sciences/Peking Union Medical College,Beijing 100730,China;Department of Pulmonary and Critical Care Medicine,China-Japan Friendship Hospital,Beijing 100029,China;Department of Medical Imaging,Second Hospital of Shandong University,Jinan 250033,China;Department of Automation,Tsinghua University,Beijing 100089,China)
出处
《计算机工程与应用》
CSCD
北大核心
2021年第11期11-20,共10页
Computer Engineering and Applications
基金
国家重点研发计划(2017YFB1400803)。
关键词
支气管扩张
肺实质提取
肺叶分割
目标检测
bronchiectasis
lung parenchyma segmentation
pulmonary lobectomy
object detection
