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纸质出版日期： 2024-09-16 ，
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楼陆飞， 应俊杰， 蔡凯俊， 辛宇. 2024. 医学影像多血管和气道分割方法综述. 中国图象图形学报， 29(09):2692-2715
Lou Lufei， Ying Junjie， Cai Kaijun， Xin Yu. 2024. Review of various vessels and airway segmentation in medical imaging. Journal of Image and Graphics， 29(09):2692-2715
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 development 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 complex 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 comprehensive 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 segmentation， 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 subsequent studies. Moreover， this paper introduces loss functions， evaluation metrics that apply to vessel and airway segmentation in medical images， and collates currently publicly available datasets on various types of vessel and airway segmentation. Finally， this paper discusses the limitations of the current methods for medical image vessel and airway segmentation and future research directions. In our research， we have identified DRIVE and STARE datasets as prevailing public benchmarks for retinal vessel segmentation tasks and established a standardized system for evaluation metrics. Such development offers valuable insights and guidance in the advancement of this field. However， in regard to cerebrovascular and coronary artery segmentation tasks， various research endeavor utilized datasets that s exhibit substantial heterogeneity and are seldom publicly accessible. Furthermore， various studies apply inconsistent evaluation metrics， which underscores the imperative need for increased attention and progress in this domain. In addition， in the context of airway segmentation tasks， numerous research have adopted custom metrics， such as Branch Detected and Tree-length Detected， which enable precise assessment based on airway-specific attributes and are highly relevant to other types of segmentation tasks. The various subtypes of vessel and airway segmentation tasks share commonalities while also displaying distinctive features. On one hand， conventional techniques， such as threshold segmentation and morphological transformations， are widely applied in each segmentation task， with a focus on the processing of raw and grayscale image data. Traditional machine learning methods predominantly depend on the application of mathematical techniques and stochastic models to improve segmentation outcomes and focus on feature enhancement and noise reduction. Conversely， deep learning methods address specific challenges unique to each domain in various tasks while adapting to particular issues encountered. In the case of retinal vessel segmentation， most research initiatives concentrate on overcoming the challenges posed by capillaries. Cerebrovascular and coronary artery segmentation face the challenges associated with data scarcity and compromised image quality due to the limited number of datasets. In addition， airway segmentation is a relatively well-explored area， with ongoing research endeavors concentrating on the improvement and completeness of segmentation coherence to augment its clinical applicability. Deep learning methods have prevailed in current research due to their capacity for multilevel feature learning. Although notable progress has been achieved in the field of medical imaging for vessels and airway segmentation via deep learning， certain limitations persist， and pressing issues warrant attention. First， vessels and airway datasets have limited sizes， which constrains the generalizability of deep-learning models and renders them susceptible to overfitting during training. Second， the application of inconsistent evaluation metrics and undisclosed datasets in most studies hampers objective comparisons among algorithms in the same field and research progress. Third， supervised methods continually dominate the vessel and airway segmentation landscape. Notably， current endeavors are aimed at addressing the shortage of adequately labeled vessel image datasets through the use of unsupervised or semisupervised deep-learning techniques. Although these methods， which include approaches such as reinforcement learning， generative networks， and recurrent networks， may not be directly applicable to clinical vessel segmentation， they are garnering increased research interest. Furthermore， evaluation of vessels and airway segmentation models solely based on metrics， such as accuracy， intersection over union， and Dice coefficient， falls short. A comprehensive and standardized evaluation framework specific to vessels and airway segmentation， which necessitates systematic and meticulous research and formulation， must be developed. In addition， the evolution of imaging technology has resulted in production of high-resolution vessel and airway images but at the cost of increased computational demands. Nonetheless， numerous clinical applications demand real-time processing， but limited attention has been devoted to addressing this issue. Consequently， future research endeavors should focus on the reduction of the computational burden of segmentation algorithms. The research on medical imaging vessels and tracheal segmentation can be directed toward several key areas. First， with the continual advancement of medical imaging technology， the integration of medical image data from various modalities has been expanding. Consequently， medical imaging vessels and airway segmentation will increasingly focus on the exploration of the segmentation of multimodal images and effective strategies for the fusion of multiple image information. Second， the precise assessment of segmentation algorithm performance and guide model improvement can be attained through the development of specialized metrics tailored to distinguishing the characteristics of vessels and airway segmentation， such as the clDice metric emphasizing connectivity. Third， the research focus will pivot toward real-time vessel and airway segmentation in medical imaging， which will facilitate immediate analysis and diagnosis， which is critical for intraoperative navigation and emergency medicine， during image acquisition. Finally， despite the high number of methods developed for medical imaging vessels and airway segmentation， a universal macromodel suitable for all applications is lacking. Another crucial direction is the development of a versatile macromodel for medical imaging vessels and airway segmentation， which will leverage powerful computational capabilities and large-scale data training to construct accurate， efficient， and broadly applicable medical-image segmentation models. This direction holds promise in the context of the complex and diverse nature of medical imaging data.
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 learningmedical image segmentationretinal vessel segmentationcerebrovascular segmentationcoronary artery segmentationairway segmentationimage processing
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