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.

通过提出 Dr-SAM，一个综合性的多阶段框架，用于分割血管、估算血管直径和异常分析的对周围血管的血管造影图像的综合分析，我们希望能够增强诊断精确性，改善血管问题患者的健康结果，并引入一个新的用于周围血管血管造影图像的基准数据集，以推动未来在这个方向的研究。

Dr-SAM: 血管图像的端到端框架，用于血管分割，直径估计和异常检测

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

Angiography is widely used to detect, diagnose, and treat cerebrovascular
diseases. While numerous techniques have been proposed to segment the vascular
network from different imaging modalities, deep learning (DL) has emerged as a
promising approach. However, existing DL methods often depend on proprietary
datasets and extensive manual annotation. Moreover, the availability of
pre-trained networks specifically for medical domains and 3D volumes is
limited. To overcome these challenges, we propose a few-shot learning approach
called VesselShot for cerebrovascular segmentation. VesselShot leverages
knowledge from a few annotated support images and mitigates the scarcity of
labeled data and the need for extensive annotation in cerebral blood vessel
segmentation. We evaluated the performance of VesselShot using the publicly
available TubeTK dataset for the segmentation task, achieving a mean Dice
coefficient (DC) of 0.62(0.03).

血管拍片是广泛用于检测、诊断和治疗脑血管疾病的方法。由于现有的深度学习方法往往依赖于专有数据集和大量的手动标注，我们提出了一种名为 VesselShot 的少样本学习方法，通过利用少量有注释的辅助图像的知识，缓解了脑血管分割中标注数据稀缺和大量标注的问题。我们使用公开可用的 TubeTK 数据集对 VesselShot 的性能进行评估，取得了平均 Dice 系数为 0.62 (0.03) 的分割结果。