Segmentation of blood vessels using rule-based and machine-learning-based methods: a review

Abstract

Vessel segmentation as a component of medical image processing is the prerequisite for accurate diagnosis of vascular-related diseases. Manual delineation of blood vessels has been turned out to be time consuming and observer dependent. Therefore, much effort has been dedicated to the automatic or semi-automatic vessel segmentation methods. Previous literatures have reviewed the state of vessel segmentation methods from various perspectives. However, their reviews did not take the modern machine-learning methods especially deep neural networks into account. In this paper, we reviewed the state-of-the-art vessel segmentation methods by dividing them into two categories, rule-based, and machine-learning-based methods. The rule-based methods discriminate vessel structure from background relying on intuitively and exquisitely designed rule sets, while the machine-learning-based methods carry out the segmentation by self-learned rules from the previous experience. Instead of exhaustively listing all vessel segmentation methods, this paper focuses on the well-known blood vessel segmentation methods in recent years, to give readers a glimpse of the current state and future direction of segmentation technique for blood vessels.