Topological distance-constrained feature descriptor learning model for vessel matching in coronary angiographies

Background: Feature matching technology is vital to establish the association between virtual and real objects in virtual reality and augmented reality systems. Specifically, it provides them with the ability to match a dynamic scene. Many image matching methods, of which most are deep learning-based, have been proposed over the past few decades. However, vessel fracture, stenosis, artifacts, high background noise, and uneven vessel gray-scale make vessel matching in coronary angiography extremely difficult. Traditional matching methods perform poorly in this regard. Methods: In this study, a topological distance-constrained feature descriptor learning model is proposed. This model regards the topology of the vasculature as the connection relationship of the centerline. The topological distance combines the geodesic distance between the input patches and constrains the descriptor network by maximizing the feature difference between connected and unconnected patches to obtain more useful potential feature relationships. Results: Matching patches of different sequences of angiographic images are generated for the experiments. The matching accuracy and stability of the proposed method is superior to those of the existing models. Conclusions: The proposed method solves the problem of matching coronary angiographies by generating a topological distance-constrained feature descriptor.