Dr-SAM: An End-to-End Framework for Vascular Segmentation Diameter Estimation and Anomaly Detection on Angiography Images.

Vazgen Zohranyan, Vagner Navasardyan, Hayk Navasardyan, Jan Borggrefe, Shant Navasardyan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5113-5121

Recent advancements in AI have significantly transformed medical imaging particularly in angiography by enhancing diagnostic precision and patient care. However existing works are limited in analyzing the aorta and iliac arteries above all for vascular anomaly detection and characterization. To close this gap we propose Dr.SAM a comprehensive multi-stage framework for vessel segmentation diameter estimation and anomaly analysis aiming to examine the peripheral vessels through angiography images. For segmentation we introduce a customized positive/negative point selection mechanism applied on top of the Segment Anything Model (SAM) specifically for medical (Angiography) images. Then we propose a morphological approach to determine the vessel diameters followed by our histogram-driven anomaly detection approach. Moreover we introduce a new benchmark dataset for the comprehensive analysis of peripheral vessel angiography images which we hope can boost the upcoming research in this direction leading to enhanced diagnostic precision and ultimately better health outcomes for individuals facing vascular issues.