Greedy Graph Searching for Vascular Tracking in Angiographic Image Sequences

TL;DR

This paper introduces a greedy graph search-based method for vascular tracking in angiographic sequences, effectively handling complex structures and image quality issues to improve diagnostic and interventional guidance.

Contribution

The study presents a novel greedy graph search approach combined with topology optimization and dynamic time warping for robust vascular tracking in challenging angiographic images.

Findings

Achieved F1 score of 0.89 on single branch dataset

Attained F1 score of 0.88 on vessel tree dataset

Demonstrated robustness and effectiveness in vascular tracking

Abstract

Vascular tracking of angiographic image sequences is one of the most clinically important tasks in the diagnostic assessment and interventional guidance of cardiac disease. However, this task can be challenging to accomplish because of unsatisfactory angiography image quality and complex vascular structures. Thus, this study proposed a new greedy graph search-based method for vascular tracking. Each vascular branch is separated from the vasculature and is tracked independently. Then, all branches are combined using topology optimization, thereby resulting in complete vasculature tracking. A gray-based image registration method was applied to determine the tracking range, and the deformation field between two consecutive frames was calculated. The vascular branch was described using a vascular centerline extraction method with multi-probability fusion-based topology optimization. We introduce an undirected acyclic graph establishment technique. A greedy search method was proposed to acquire all possible paths in the graph that might match the tracked vascular branch. The final tracking result was selected by branch matching using dynamic time warping with a DAISY descriptor. The solution to the problem reflected both the spatial and textural information between successive frames. Experimental results demonstrated that the proposed method was effective and robust for vascular tracking, attaining a F1 score of 0.89 on a single branch dataset and 0.88 on a vessel tree dataset. This approach provided a universal solution to address the problem of filamentary structure tracking.