Paper Info
Title
Automated Volumetric Intravascular Plaque Classification Using Optical Coherence Tomography.
Abstract
An estimated 17.5 million people died from a cardiovascular disease in 2012, representing 31 percent of all global deaths. Most acute coronary events result from rupture of the protective fibrous cap overlying an atherosclerotic plaque. The task of early identification of plaque types that can potentially rupture is, therefore, of great importance. The state-of-the-art approach to imaging blood vessels is intravascular optical coherence tomography (IVOCT). However, currently, this is an offline approach where the images are first collected and then manually analyzed an image at a time to identify regions at risk of thrombosis. This process is extremely laborious, time consuming, and prone to human error. We are building a system that, when complete, will provide interactive three-dimensional visualization of a blood vessel as an IVOCT is in progress. The visualization will highlight different plaque types and enable quick identification of regions at risk. In this article, we describe our approach, focusing on machine-learning methods that are a key enabling technology. Our empirical results using real OCT data show that our approach can identify different plaque types efficiently with high accuracy across multiple patients.
Year
DOI
Venue
2017
10.1609/aimag.v38i1.2713
AI MAGAZINE
Field
DocType
Volume
Biomedical engineering,Computer vision,Optical coherence tomography,Computer science,Visualization,Fibrous cap,Human error,Interactive 3d,Thrombosis,Artificial intelligence
Journal
38
Issue
ISSN
Citations 
1
0738-4602
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Ronny Shalev101.01
Daisuke Nakamura200.68
Setsu Nishino300.68
Andrew M Rollins451.49
Hiram G. Bezerra5106.64
David L. Wilson617436.04
Soumya Ray7948.89