Towards Flexible and Universal: A Novel Endpoint-based Framework for Vessel Structural Information Extraction

In computer-assisted intravascular interventional surgery, extracting detailed information of target vessels from X-ray angiographic images can be meaningful in improving safety and effectiveness. However, large amounts of effort have been dedicated to segmenting the whole blood vessels from the background while ignoring the internal structure, which is limited in clinical application. In this paper, we propose a flexible and universal endpoint-based framework for vessel structural information extraction. The framework first localizes all the endpoints of target vessel segments through a Coarse-to-Fine Keypoint Detection Network (CFKD-Net), in which the designed Multi-branch Feature Aggregation (MFA) module captures both in-patch and cross-patch information to help recognize the points of interest based on global structure. A novel MaskMSELoss is also proposed to disambiguate those irrelevant responses. Then a designed VEssel Segmentation and Analysis (VESA) algorithm will generate the segmentation mask and morphological analysis for each vessel segment simply based on the endpoints. It can also be flexibly applied to analyze variant blood vessels which are not pre-defined before. Extensive experiments on two different coronary artery datasets consistently demonstrate that this framework can achieve state-of-the-art detection performance and successfully extract and analyze target vessel segments. Since the framework shows excellent performance on the coronary arteries with severe deformation and strong noise, it is highly promising for analyzing other vascular images.