학술논문

Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques
Document Type
Conference
Source
2019 International Conference on Computational Science and Computational Intelligence (CSCI) CSCI Computational Science and Computational Intelligence (CSCI), 2019 International Conference on. :584-589 Dec, 2019
Subject
Computing and Processing
Arteries
Manuals
Diseases
Medical diagnostic imaging
Machine learning
Image segmentation
Calcification, Aorta, Artificial Intelligence Peripheral Arterial Disease, Computed Tomography Angiogram
Language
Abstract
We report an object tracking algorithm that combines geometrical constraints, thresholding, and motion detection for tracking of the descending aorta and the network of major arteries that branch from the aorta including the iliac and femoral arteries. Using our automated identification and analysis, arterial system was identified with more than 85% success when compared to human annotation. Furthermore, the reported automated system is capable of producing a stenosis profile, and a calcification score similar to the Agatston score. The use of stenosis and calcification profiles will lead to the development of better-informed diagnostic and prognostic tools.