  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.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning