2020/12/16 by Dengqiang Jia, Jia, Dengqiang, Xiahai Zhuang +1
Computer Science · Engineering · Medicine · #Cardiovascular Health and Disease Prevention #Cerebrovascular and Carotid Artery Diseases #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Retinal Imaging and Analysis #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2012.08929
19 pages, 3 figures, 9 tables, accept by Computerized Medical Imaging and Graphics
arxiv created 2020/12/16 · openalex publication_date 2020/12/16 · arxiv updated 2020/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the conventional machine-learning-based algorithms are reviewed, and then, a general survey of deep-learning-based frameworks is provided. On the basis of the reviewed methods, the evaluation issues are introduced. The paper is concluded with discussions about the remaining exigencies and future research.