摘要
医学影像分析中,血管和气道分割是备受关注的研究。通过对血管和气道异常的评估,例如动脉壁增厚和硬化、脑血管破裂导致的出血以及肺部或气道内的肿瘤等,可以实现此类疾病的早期诊断和临床治疗指导。随着医学成像技术的发展,影像分割技术在评估和诊断这些结构异常方面变得越来越重要。然而,由于其复杂的结构和病理变化,血管和气道的准确分割仍然是一项具有挑战性的任务。许多研究都集中在特定类型的血管或气道分割上,对多种类型的血管和气道分割方法的综合回顾相对缺乏。对各类血管和气道的综合回顾可以为医学专家和研究人员提供更全面的临床参考价值。此外,不同类型的血管和气道具有形态上的相似性,一些算法和技术可以同时应用于它们的分割中,综合回顾也增强了讨论的广泛性。因此,本文对近20年来具有代表性的视网膜血管分割、脑血管分割、冠状动脉分割和气道分割4类研究工作进行了归纳,分别从传统方法、机器学习方法和深度学习方法3个方面对每类研究进行综述,同时总结了各种方法的优缺点,为后续研究提供了理论参考。此外,本文还介绍了适用于医学影像血管和气道分割的损失函数、评价指标,并收集了目前公开的各类血管和气道分割数据集。最后,本文讨论了目前医学影像血管和气道分割方法的局限性以及未来研究的方向。
Vessel and airway segmentation are arouse considerable interest in medical image analysis.Vessel and airway abnormalities,such as thickening and sclerosis of arterial walls,bleeding due to cerebrovascular rupture,and tumors in lungs or airways,must be evaluated for the corresponding early diagnosis and clinical treatment guidance.The develop⁃ment of medical imaging technology made image segmentation techniques important in the evaluation and diagnosis of such structural abnormalities.However,the accurate segmentation of vessels and airways presents a challenge due to their com⁃plex structural and pathological variations.Most studies have focused on specific types of vessels or airway segmentation,and comprehensive reviews of various vessel types and airway segmentation methods are relatively lacking.Medical experts and researchers can benefit from a comprehensive review of all types of vessels and airways,which can serve as a compre⁃hensive clinical reference.In addition,various types of vessels and airways show morphological similarities,and certain algorithms and techniques can be simultaneously applied in their segmentation,with a comprehensive review expanding the breadth of discussion.Therefore,this paper summarizes four types of representative research on retinal vessel segmenta⁃tion,cerebral vessel segmentation,coronary artery segmentation,and airway segmentation in the past two decades and reviews each type of research from three aspects:traditional,machine learning,and deep learning methods,In addition,this review summarizes the advantages and disadvantages of these various methods to provide theoretical references for sub⁃sequent studies.Moreover,this paper introduces loss functions,evaluation metrics that apply to vessel and airway segmen⁃tation in medical images,and collates currently publicly available datasets on various types of vessel and airway segmenta⁃tion.Finally,this paper discusses the limitations of the current methods for medical image vessel and airway segmentation and futur
作者
楼陆飞
应俊杰
蔡凯俊
辛宇
Lou Lufei;Ying Junjie;Cai Kaijun;Xin Yu(College of Information Science and Engineering,Ningbo University,Ningbo 315211,China;Key Laboratory of Mobile Network Application Technology of Zhejiang Province,Ningbo 315211,China)
出处
《中国图象图形学报》
CSCD
北大核心
2024年第9期2692-2715,共24页
Journal of Image and Graphics
基金
浙江省自然科学基金项目(LY22F020001)
宁波市“泛3315”计划项目(2019B-18-G)。
关键词
深度学习
医学影像分割
视网膜血管分割
脑血管分割
冠状动脉分割
气道分割
图像处理
deep learning
medical image segmentation
retinal vessel segmentation
cerebrovascular segmentation
coro⁃nary artery segmentation
airway segmentation
image processing
