Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques

被引：3

作者：

Zhao, Liang ^{[1
]}

Odigwe, Brendan ^{[1
]}

Lessner, Susan ^{[2
]}

Clair, Daniel G. ^{[3
]}

Mussa, Firas ^{[4
]}

Valafar, Homayoun ^{[1
]}

机构：

[1] Univ South Carolina, Comp Sci & Engn, Columbia, SC 29208 USA

[2] Univ South Carolina, Sch Med, Cell Biol & Anat, Columbia, SC 29208 USA

[3] Univ South Carolina, Sch Med, Surg, Columbia, SC 29208 USA

[4] Zucker Sch Med, New Hyde Pk, NY USA

来源：

2019 6TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI 2019) | 2019年

关键词：

Calcjfication; Aorta; Artjficial Intelligence; Peripheral Arterial Disease; Computed Tomography Angiogram; NATIONAL-HEALTH; SEGMENTATION; PREVALENCE; DISEASE; RISK;

D O I：

10.1109/CSCI49370.2019.00110

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We report an object tracking algorithm that combines geometrical constraints, thresh olding, and motion detection for tracking ofthe descending aorta and the network of major arteries that branch from the aorta including the iliac andfrmoral arteries. Using our automated identjflcation and analysis, arterial system was identWed with more than 85% success when compared to human annotation. Furthermore, the reported automated system is capable of producing a stenosis profile, and a calcjfication score similar to the Agatston score. The use of stenosis and calcjfication profiles will lead to the development of better-informed diagnostic and progn ostic tooLs.

引用

页码：584 / 589

页数：6

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