  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.
