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

TL;DR

Dr-SAM is an end-to-end framework that improves vascular analysis in angiography images by combining vessel segmentation, diameter estimation, and anomaly detection, supported by a new benchmark dataset.

Contribution

It introduces a novel multi-stage framework with a customized segmentation model, a morphological diameter estimation method, and a histogram-driven anomaly detection approach, along with a new dataset.

Findings

Enhanced vessel segmentation accuracy on angiography images

Effective diameter estimation for peripheral vessels

Improved anomaly detection capabilities

Abstract

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.